In 2025, data engineering is no longer just a back-office function—it has become the very foundation upon which modern businesses operate. The explosion of digital transformation initiatives, the widespread integration of artificial intelligence, and the demand for real-time analytics have elevated this discipline to a strategic level. Organizations across industries now rely on robust data infrastructures to make critical decisions, innovate at scale, and gain a competitive edge. The relevance of data engineering stems from its unique ability to bridge the gap between raw, unstructured data and actionable insights. Without skilled data engineers, even the most sophisticated machine learning models or analytics platforms would be rendered ineffective, starved of quality data. This career path thrives in an environment where speed, scalability, and security are equally important, and where every byte of information holds potential value. In this context, a data engineer is no longer simply a technical specialist; they are a business enabler whose work underpins the strategic vision of the organization.
This shift in importance has been accelerated by factors such as the rapid adoption of cloud-native technologies, the rise of Internet of Things ecosystems, and the mainstreaming of predictive and generative AI. Modern companies now deal with petabytes of data daily, coming from disparate and often messy sources. In this reality, the data engineer is the architect and the craftsman—designing scalable pipelines, ensuring data integrity, and building the frameworks that allow analysts, data scientists, and AI systems to work effectively. Choosing to enter this field in 2025 means stepping into a space where technical mastery meets business impact, where your skills directly influence decision-making speed, product innovation, and customer experience. It is not a role confined to the server room; it is a central position in the digital command center of any forward-thinking enterprise.
Choosing the Data Engineering Path in a Shifting Global Economy
The global economy in 2025 demands professionals who can work at the intersection of technology, strategy, and adaptability. Automation is reshaping industries, and organizations that fail to modernize their data infrastructure risk falling behind irreversibly. Choosing data engineering as a career path today is both a strategic and resilient move. While certain jobs are being phased out due to AI, data engineering has been amplified by it—AI systems require massive, clean, and well-structured datasets to function, and this dependency keeps skilled data engineers in high demand. Beyond AI, businesses are also rethinking how they manage risk, optimize operations, and personalize customer engagement—all areas where strong data pipelines and efficient storage solutions make the difference between stagnation and growth.
From a personal career standpoint, the field offers stability and scalability. The demand for data engineers is not restricted to tech companies; it spans sectors such as healthcare, finance, retail, manufacturing, energy, and even government operations. This diversity provides a buffer against market volatility and opens the door to countless opportunities for specialization. Someone entering data engineering now can expect to remain relevant for decades if they commit to continuous learning and skill refinement. Furthermore, it is a career that offers both horizontal and vertical growth. One can deepen their expertise by mastering cloud data architectures, streaming analytics, or big data security, or transition into leadership roles such as data platform manager, solutions architect, or chief data officer. Choosing this career path today is not merely about job security—it is about positioning oneself at the helm of technological progress, where decisions are data-driven and innovation thrives on infrastructure excellence.
Defining the Role of a Data Engineer in the Modern Era
The modern data engineer operates in a far more complex and fast-paced environment than their predecessors. Their responsibilities extend beyond writing ETL (Extract, Transform, Load) scripts or managing databases. They are deeply involved in designing and maintaining large-scale data systems that integrate structured and unstructured data from multiple sources—social media feeds, IoT sensors, transaction records, CRM platforms, and more. This involves collaborating with data scientists to ensure datasets are optimized for advanced analytics, working alongside DevOps teams to maintain performance efficiency, and partnering with cybersecurity experts to protect sensitive information.
A data engineer must also be adept at implementing solutions that can adapt to changing business requirements without significant downtime. This includes embracing event-driven architectures, stream processing, and microservices-based pipelines that allow organizations to handle both real-time and batch data needs effectively. In the age of distributed computing, their role also encompasses the selection and integration of cloud-based platforms such as AWS Redshift, Google BigQuery, Azure Synapse, or Snowflake, each with its unique trade-offs in cost, performance, and scalability.
However, technical skill alone is not enough. The modern data engineer must possess a strategic mindset—understanding the business objectives behind each project, anticipating future scaling challenges, and designing systems that are resilient to both technological and market disruptions. They are part analyst, part engineer, and part visionary, ensuring that the organization’s data backbone is strong enough to support innovation while flexible enough to evolve with emerging trends. In essence, they are the unsung heroes of the digital transformation era, quietly enabling every customer insight, predictive model, and AI breakthrough.
Building the Foundation: Education, Skills, and Programming Proficiency
The pathway into data engineering can be as traditional or as unconventional as the candidate chooses. Many professionals come from formal computer science, software engineering, or information systems backgrounds. Others transition from adjacent fields such as database administration, business intelligence, or even physics and mathematics, leveraging their analytical strengths to adapt to data engineering requirements. In recent years, bootcamps, online certifications, and self-directed learning have created viable alternative routes, allowing motivated individuals to bypass the traditional degree structure and still land competitive roles. The key is not the path itself but the mastery of core skills that are non-negotiable in this domain.
Among these, programming proficiency stands at the forefront. Languages such as Python, Java, and Scala dominate the landscape due to their versatility and compatibility with big data tools. Python, in particular, has become indispensable for building and orchestrating pipelines, integrating APIs, and performing data manipulation tasks efficiently. SQL remains equally critical, serving as the lingua franca of data querying and manipulation across relational databases. Mastery of SQL means more than basic SELECT statements; it demands fluency in writing optimized queries, designing normalized schemas, and understanding execution plans to minimize performance bottlenecks.
Beyond syntax, programming in data engineering is about solving real-world data problems—handling missing values, ensuring data consistency across systems, and optimizing storage formats for both speed and cost. This calls for an engineer’s mindset: precision, scalability, and a willingness to test and iterate relentlessly. As datasets grow larger and more complex, the ability to write clean, efficient, and well-documented code becomes an asset that can make or break a project. Pair this with strong data modeling skills, an understanding of distributed systems, and a readiness to embrace continuous learning, and you have the blueprint for a thriving data engineering career in 2025.
Mastering Databases and Data Warehousing in the Modern Data Landscape
In 2025, the distinction between merely storing data and truly harnessing it has never been sharper. At the heart of any functional data infrastructure lies a deep understanding of databases—both SQL and NoSQL—and the strategic use of data warehousing systems. For a data engineer, the database is not simply a repository; it is the living foundation upon which analytics, machine learning, and business intelligence thrive. Relational databases, governed by SQL, continue to serve as the backbone for structured data. These systems excel in transactional integrity, complex querying, and ensuring consistency across vast datasets. A mastery of SQL goes beyond writing straightforward queries; it involves optimizing joins, indexing tables for performance, partitioning data for scale, and understanding normalization versus denormalization trade-offs.
On the other hand, NoSQL databases have emerged as indispensable in dealing with the fluid, often unpredictable nature of modern data. Document stores like MongoDB, columnar databases such as Cassandra, and graph databases like Neo4j address needs that relational systems cannot easily meet. They allow for flexible schemas, rapid ingestion, and high availability across distributed environments—qualities that are essential in real-time applications, content management, and IoT ecosystems. Knowing when to choose SQL over NoSQL—or how to architect hybrid systems—becomes a hallmark of a skilled data engineer.
Data warehousing extends this expertise into the realm of large-scale analytics. Platforms like Snowflake, Google BigQuery, and Amazon Redshift are no longer optional luxuries; they are the default expectation for organizations that must synthesize vast volumes of historical and streaming data into cohesive, actionable narratives. A data engineer proficient in these environments can design schema architectures that minimize storage costs while maximizing query efficiency, implement materialized views for repeated insights, and leverage features such as clustering and partitioning for optimal performance. In essence, mastery over both transactional databases and analytical warehouses allows a data engineer to not only store information but to craft the structural DNA that determines how easily and effectively it can be retrieved, understood, and acted upon.
Navigating ETL and the Evolution into ELT for Batch and Streaming Data
Once the data is housed in robust systems, the next challenge lies in moving, shaping, and transforming it efficiently. This is where ETL—Extract, Transform, Load—becomes a central discipline. In traditional data environments, ETL pipelines were the workhorses that gathered data from multiple sources, cleaned and standardized it, and then deposited it into target systems. In the modern cloud-driven ecosystem, ELT—Extract, Load, Transform—has emerged as an alternative pattern, capitalizing on the processing power of cloud warehouses to handle transformations after the data is loaded.
A data engineer in 2025 must be fluent in both approaches. Batch processing remains critical for jobs that run at scheduled intervals, aggregating data into reports, trend analyses, and historical archives. This mode is essential for financial reconciliations, large-scale backups, and compliance-driven audits. Streaming, however, is the heartbeat of real-time analytics—whether it is detecting fraudulent transactions as they occur, delivering personalized recommendations in milliseconds, or monitoring supply chain telemetry to avert disruptions before they escalate. Technologies like Apache Kafka, Apache Flink, and Google Dataflow are no longer niche skills but expected tools in the arsenal.
The artistry in ETL and streaming lies not only in technical implementation but in designing pipelines that are fault-tolerant, scalable, and adaptable to shifting requirements. Data engineers must anticipate schema changes, handle malformed records gracefully, and ensure that data lineage is meticulously tracked for compliance and trustworthiness. The modern ETL process is a dance between automation and control, where the goal is not simply to move data from point A to point B but to shepherd it through a journey of refinement so that by the time it reaches analysts, it is both clean and contextually rich. The engineer who can design pipelines that are both robust and elegant becomes an invaluable asset to any data-driven organization.
Bringing Concepts to Life Through Practical Projects
Theory becomes truly valuable when it is applied, and in the realm of data engineering, beginner projects are the proving ground where concepts crystallize into skills. Designing and building a small-scale data warehouse for a mock e-commerce company, for instance, offers a controlled yet realistic environment for applying SQL design principles, integrating NoSQL components for user-generated content, and orchestrating ETL pipelines to populate the warehouse with transactional data.
Another entry point could be constructing a streaming data pipeline that ingests tweets in real time, classifies them by sentiment, and stores aggregated results in a cloud database for visualization. This type of project bridges the gap between raw, unstructured data and actionable insight, all while teaching key skills like API integration, message queue handling, and the balancing act between throughput and latency. For those interested in data warehousing optimization, a project that experiments with different table partitioning strategies or storage formats can provide deep insight into how architecture choices directly impact query performance and cost efficiency.
What makes these projects valuable is not just the technical execution but the mindset they foster. They encourage problem decomposition, iterative improvement, and the ability to foresee bottlenecks before they cripple a system. They also offer tangible artifacts for portfolios, proving to prospective employers that the engineer can not only talk about data pipelines but build them from scratch. More importantly, they cultivate the adaptability that is essential in the real world, where no dataset is perfectly clean, no client requirement clear, and no system is immune to change.
Data as the Invisible Architecture of the Modern World
In the grand narrative of technological progress, data has become the invisible architecture shaping the modern world. It is the silent force behind decisions that affect economies, cities, and individual lives. When a logistics company reroutes its fleet to avoid a storm, when a hospital predicts patient admission surges to allocate resources, when an online store adjusts prices dynamically in response to demand—all of these are acts of data in motion, orchestrated by infrastructures that most people never see. In 2025, the volume, velocity, and variety of this data have reached levels once considered unimaginable, yet our ability to interpret and act upon it has grown in parallel thanks to advancements in data engineering.
The implications are profound. Data is no longer just a record of what has happened—it is a predictive model of what might happen next. It can warn of impending crises, uncover hidden inefficiencies, and reveal opportunities invisible to intuition alone. For societies, it holds the potential to drive sustainability, reduce waste, and make governance more transparent. For individuals, it can personalize healthcare, education, and even leisure in ways that feel almost prescient.
Yet with this power comes responsibility. The data engineer sits at a critical junction where ethical considerations must be woven into technical designs. Decisions about data retention, anonymization, and access control are not mere compliance exercises—they are acts of stewardship over the trust that users and communities place in organizations. This is why the craft of building databases, designing ETL pipelines, and structuring warehouses is far more than an engineering pursuit; it is a role in shaping the ethical, efficient, and equitable future of the world’s digital fabric. The systems we build today will determine the quality of insights tomorrow, and by extension, the quality of the decisions that will shape our collective destiny.
Embracing Cloud Computing as the Backbone of Modern Data Engineering
In the landscape of 2025, cloud computing is no longer a technological trend but the de facto foundation for data engineering at scale. The role of the modern data engineer is inseparable from the cloud, where platforms such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure offer the flexibility, scalability, and resilience that on-premises infrastructure cannot match. Each platform presents its own ecosystem of services, from managed databases and serverless compute options to machine learning integration and advanced analytics. AWS boasts an expansive catalog of data-centric tools like Redshift, Glue, and Kinesis that enable both batch and streaming workflows. GCP offers BigQuery for lightning-fast analytical queries, Dataflow for unified stream and batch processing, and Vertex AI for seamless machine learning deployment. Azure provides Synapse Analytics, Data Factory, and a deeply integrated environment for hybrid workloads.
For a data engineer, the choice of cloud platform is both strategic and situational. It involves weighing factors like existing enterprise commitments, compliance requirements, latency needs, and long-term scalability. Mastery here does not mean learning every service in isolation—it means understanding how to integrate services into coherent architectures that are cost-effective, fault-tolerant, and future-ready. This requires proficiency in infrastructure-as-code tools like Terraform or AWS CloudFormation, ensuring that deployments are reproducible and version-controlled. It also demands a keen awareness of security models, including identity and access management, encryption protocols, and compliance certifications relevant to industries such as healthcare or finance. In the cloud-first era, the engineer is an architect not just of pipelines but of environments, crafting ecosystems that can evolve alongside the data they serve.
Harnessing Big Data Tools for Distributed Processing Power
While cloud platforms provide the infrastructure, the engines of large-scale data processing remain the big data frameworks that have matured and expanded over the past decade. Hadoop, once the trailblazer of distributed storage and processing, has given rise to more agile and memory-optimized systems such as Apache Spark and Apache Flink. Spark’s in-memory computation capabilities have revolutionized data transformation speeds, making it indispensable for workloads that require iterative algorithms or complex joins across vast datasets. Flink, with its native support for stateful stream processing, has emerged as a powerhouse for real-time analytics and event-driven architectures.
In mastering these tools, a data engineer learns more than syntax and configuration—they acquire an understanding of distributed computing principles. This includes partitioning strategies, data locality optimization, and handling fault tolerance in a cluster environment. These skills are essential for ensuring that a job running across hundreds or thousands of nodes completes reliably and within the expected time frame. Just as importantly, proficiency in big data frameworks allows engineers to integrate machine learning pipelines at scale, enabling predictive and prescriptive analytics across massive, continuously updated datasets.
The modern data engineer must also navigate the integration of these tools into hybrid and multi-cloud environments, orchestrating workflows that span different storage backends and compute clusters. They must weigh trade-offs between streaming-first architectures and micro-batch designs, knowing when each approach aligns with business requirements. By mastering these frameworks, the engineer gains the ability to process not just more data, but more meaningful data—transforming raw streams into refined, decision-ready insights in near real time.
Designing and Orchestrating Scalable Data Pipelines
At the heart of modern data engineering lies the art and science of pipeline orchestration. In an age when data flows in from countless sources at unpredictable speeds, scalability is not a luxury—it is an operational necessity. The role of the engineer is to design pipelines that can gracefully handle surges in data volume, changes in schema, and shifts in processing logic without collapsing under pressure. This often means leveraging orchestration tools such as Apache Airflow, Prefect, or Dagster to manage dependencies, schedule tasks, and monitor execution across complex workflows.
Scalability in this context extends beyond raw capacity. It means creating modular pipelines where components can be independently developed, tested, and deployed. It means building fault-tolerant designs where failures in one stage can be retried or rerouted without bringing the entire system to a halt. It also means optimizing for cost, ensuring that resources scale dynamically with demand rather than consuming unnecessary compute power during low-traffic periods. Cloud-native services like AWS Step Functions or GCP’s Cloud Composer integrate orchestration directly with cloud infrastructure, allowing for tighter coupling between data processing and the environments that host it.
The orchestration challenge is as much about people as it is about technology. Pipelines must be transparent enough for teams to debug collaboratively, documented enough to onboard new engineers efficiently, and secure enough to comply with data governance policies. The most effective data engineers understand that scalability is not only a matter of throughput but of adaptability, ensuring that pipelines remain relevant as data sources, tools, and business priorities evolve.
The Evolution of Data Systems and the Transformation of the Enterprise
Over the past two decades, data systems have undergone a transformation as profound as any technological revolution in history. What began as isolated databases in back-office servers has evolved into sprawling, interconnected ecosystems capable of ingesting, processing, and analyzing data from across the globe in real time. The enterprise of 2025 exists in a perpetual state of adaptation, where competitive advantage hinges on the ability to transform raw information into strategic action at unprecedented speeds. This shift has redefined industries, creating new business models, collapsing inefficiencies, and empowering organizations to predict and shape market trends rather than merely react to them.
At the center of this evolution are data engineers, whose work determines the pace at which ideas become realities. The systems they design not only manage existing information but also anticipate the unknown—building architectures resilient enough to accommodate new data types, emerging technologies, and shifting regulatory landscapes. The enterprise transformation driven by these systems is both technological and cultural. Decision-making is no longer confined to executive boardrooms; with the right data infrastructure, actionable insights flow to every level of the organization, empowering frontline workers, product teams, and customer service representatives alike.
This democratization of data has profound implications for innovation. In a connected world, the barriers between industries blur; a retail company can operate like a technology firm, a hospital can function with the efficiency of a logistics giant, and a manufacturing plant can make predictive maintenance decisions with the foresight of an analytics consultancy. The evolution of data systems has made this possible, but the transformation is far from complete. As AI, IoT, and decentralized data architectures continue to mature, the organizations best prepared to leverage them will be those that treat data engineering not as a support function, but as a strategic pillar of their identity. The engineers who understand this truth will find themselves at the forefront of shaping not only enterprise systems but the future of how the modern world thinks, works, and innovates.
Building Real-World Experience and a Standout Portfolio
In the competitive world of data engineering, theoretical knowledge and certifications are valuable, but they are only the starting point. True professional credibility comes from demonstrable, real-world experience—projects that showcase not only your technical competence but also your ability to solve practical problems under constraints. Building a portfolio is not a vanity exercise; it is your living proof of capability, a curated narrative of what you can create when presented with raw challenges and ambitious goals. Real-world projects push you into uncharted territory, where documentation may be incomplete, data might be messy, and business requirements are likely to change midstream. These environments force you to think creatively, prioritize effectively, and find solutions that balance precision, speed, and scalability.
For aspiring data engineers, a strong portfolio might include case studies of designing and deploying end-to-end data pipelines, integrating multiple data sources into a unified warehouse, or optimizing ETL processes to cut latency and costs. It might highlight real-time analytics dashboards built on streaming frameworks, or a machine learning integration project that uses predictive models to enhance decision-making. Each project should tell a story—what the problem was, how you approached it, what tools you used, and the tangible results achieved. Including code repositories, architectural diagrams, and live demos where possible adds depth and credibility. In the modern hiring landscape, where technical interviews often combine problem-solving exercises with portfolio reviews, having a tangible record of achievement can be the deciding factor that elevates you above equally qualified peers.
Advancing with Specialized Skills in a Data-Driven World
Once foundational skills are mastered, advancing in data engineering requires an intentional focus on specialized knowledge areas that align with both your interests and market demand. Machine learning fundamentals are increasingly becoming a natural extension of data engineering expertise, as the boundaries between building pipelines and feeding predictive models continue to blur. Understanding how to prepare feature sets, optimize training data pipelines, and manage model outputs positions you as a bridge between data science and engineering—a role many organizations urgently need.
Governance and security are equally critical, especially as data privacy regulations tighten worldwide. Skills in data cataloging, lineage tracking, and compliance monitoring ensure that pipelines do not merely deliver data quickly, but deliver it in a way that meets legal, ethical, and organizational standards. Security mastery involves encrypting data in transit and at rest, implementing fine-grained access controls, and designing architectures resilient against breaches and misuse. These competencies are no longer optional; they are foundational to operating in industries such as finance, healthcare, and government, where lapses can result in catastrophic losses and reputational damage.
Specialization can also emerge through platform or domain expertise. You might become a sought-after expert in a specific cloud environment such as AWS, Azure, or GCP, mastering not only the services but the ecosystem of integrations and cost-optimization strategies they require. Alternatively, you might focus on a particular industry vertical, learning its data challenges in depth and becoming a trusted advisor for building solutions tailored to that sector’s unique needs. These advanced capabilities allow you to position yourself not just as a data engineer, but as a strategic partner in driving an organization’s innovation agenda.
Career Trajectories, Market Demand, and Salary Potential
The career trajectory of a skilled data engineer in 2025 is as varied as it is promising. Some choose to deepen their craft, evolving into principal or lead data engineers responsible for designing enterprise-wide architectures. Others branch into solution architecture, blending engineering expertise with stakeholder engagement to translate business needs into technical blueprints. Still others pivot toward management, guiding teams of engineers while shaping the broader data strategy of the organization.
The market demand for data engineers remains high and is expected to continue climbing as organizations accumulate more data than they can readily manage or exploit. According to industry reports, companies are increasingly shifting budget and hiring priority toward roles that can transform their raw information assets into measurable business value. This demand translates into competitive salaries that often outpace other technology roles, with entry-level engineers in high-demand regions commanding salaries above the median for all IT jobs, and experienced specialists earning into the upper six figures or more. Cloud expertise, big data mastery, and strong governance skills are salary multipliers, as is proven experience in leading complex, high-stakes projects.
The global nature of the role also means location flexibility. Many organizations now embrace remote or hybrid arrangements for their data engineering teams, broadening the field of opportunities available to those who can demonstrate excellence regardless of where they are based. With such favorable conditions, career progression is less about finding opportunities and more about choosing the right ones—roles that challenge you, expand your expertise, and align with your vision for the future.
Innovation, Fulfillment, and the Expanding Horizon of Data Engineering
The story of data engineering is ultimately one of transformation—not only for enterprises but for the professionals who dedicate themselves to the craft. It is a career where technical expertise becomes a vehicle for innovation, where the solutions you build can change the trajectory of a company, a community, or even an entire industry. For many, the fulfillment lies in the tangible impact of their work: a healthcare provider able to detect disease patterns earlier, a logistics company reducing emissions through smarter routing, or a startup finding its competitive edge through customer insights delivered in real time.
The industry’s growth shows no signs of slowing. As artificial intelligence, machine learning, and automation evolve, they will depend on ever more sophisticated data infrastructures—systems that are secure, compliant, and capable of processing massive, heterogeneous datasets in seconds. The future will demand engineers who can not only keep pace with these advances but also anticipate and shape them. This will mean adopting new paradigms, experimenting with emerging technologies, and fostering a mindset of lifelong learning.
In this evolving environment, career fulfillment will increasingly come from embracing the dual identity of craftsman and innovator. As a craftsman, you refine the precision and efficiency of your systems; as an innovator, you explore how those systems can enable possibilities that were previously unthinkable. The convergence of these roles positions data engineers not merely as builders of infrastructure, but as architects of transformation in a digital-first world. It is here—in the fusion of skill, curiosity, and purpose—that the future of data engineering becomes not just a career path, but a profound and ongoing journey toward shaping the way the modern world understands and acts upon its most valuable resource: information.
Conclusion
The journey into data engineering in 2025 is not merely a career choice—it is an invitation to participate in one of the most transformative forces shaping the modern world. From mastering foundational programming skills to designing complex, scalable data architectures in the cloud, each stage builds a layered expertise that is both deeply technical and strategically influential. The data engineer of today operates at the crossroads of technology, business, and innovation, translating raw, chaotic information into systems that power real-time decisions, predictive models, and industry-defining breakthroughs.
What makes this field so compelling is its balance of intellectual rigor and creative problem-solving. It is not a static discipline; it evolves with every technological leap, every regulatory shift, and every emerging data source. This dynamism ensures that a career in data engineering remains both challenging and rewarding, demanding a mindset that embraces adaptability, curiosity, and a commitment to lifelong learning. The tools, platforms, and frameworks may change, but the core mission—building trust in data and enabling its intelligent use—remains constant.
For those entering or advancing within this space, the future holds extraordinary promise. Opportunities span industries and geographies, offering roles that range from technical specialist to visionary leader. Salaries reflect the strategic value placed on those who can harness data effectively, while the work itself offers the satisfaction of seeing ideas translated into measurable impact. Whether enabling a hospital to predict patient surges, a financial firm to detect fraud in milliseconds, or a retailer to understand its customers with unprecedented clarity, data engineers are the unseen architects of progress.
Ultimately, data engineering is more than a job title—it is a commitment to shaping the infrastructure of tomorrow’s innovations. It is a profession for those who thrive on complexity, who find fulfillment in precision, and who see possibility in every dataset. As the world continues to generate information at an exponential pace, the need for skilled, ethical, and visionary data engineers will only grow. The question is not whether this field will remain relevant, but how far those who embrace it will go in defining the next era of technological and societal transformation.