Data Engineering: Decoding the Force Driving Business Advancement

Em Blog Main Image Data Engineering

From commercial organizations to public sector departments to educational institutions, the role of the data engineer is being recognized by several key groups across the spectrum. The same holds when you look inside, from the executive suite to data scientists and business analysts to product engineers and many others from individual teams throughout a firm that directly interact with or depend on data. To this point, the role can be found in many organizations across the United States, which is home to more than 10,573 employed data engineers.1

In this first post of the two-part series, we explore the relationship of data engineering with the business and the individual teams within it as we uncover the data engineers’ pivotal role in shaping a company’s data infrastructure and operations. We will also present the role’s scope and responsibilities connected to the value it has on innovation as a critical element in driving business growth in a repeatable and sustainable manner.

The Central Function of the Data Engineering Role

Data engineering is closely linked to roles like data science and analytics but can differ considerably in focus and responsibility. Like data scientists and data analysts, data engineers work extensively with data. However, while data scientists are tasked with applying advanced statistical models to generate insights and predictions and data analysts spend their time extracting meaningful conclusions to influence business decisions, data engineers are busy constructing the tech stack foundation that enables these tasks.

Their work involves managing large datasets, constructing data pipelines, and moving data from source systems to data warehouses and data lakes. Their work ensures that the infrastructure supports high-quality, resilient, and efficient data generation, storage, retrieval, and processing. This is where the connection to the other roles comes back into play, as the data engineers enable the work of their counterparts in data science and analysis.

Playing an integral role in any organization, data engineers are predominantly responsible for defining, maintaining, and optimizing a company’s data infrastructure, channeling data from diverse sources to centralized data repositories that operate information-driven business workflows smoothly.

Some of the primary responsibilities of data engineers include:

  • Model Design: Defining data assets, modeling, and database design to create structures supporting business requirements.
  • Architecture Strategy: Working with various business units and engineering teams to strategize for a long-term, resilient data platform architecture.
  • Infrastructure Delivery: Developing, constructing, testing, and maintaining databases and large-scale data processing systems.
  • Systems Management: Overseeing the interoperability of data architecture, databases, and processing systems.
  • Pipeline Development: Developing and maintaining scalable data pipelines and flexible API integrations to handle increasing data volume and complexity.
  • Data Integration: Managing data integration and related processes to build pipelines for data transformation and movement.
  • Quality Assurance: Implementing systems to monitor data quality and integrity, ensuring accurate and readily available data for stakeholders and business processes.
  • Issue Resolution: Performing data analysis to troubleshoot and resolve data-related issues.

Since the data engineering role is symbiotic with the data scientists and analysts, it makes sense that team collaboration is also a critical element of successfully integrating this role across the company. Collaborating with analytics and business teams can help to uncover and enhance data models, foster data accessibility, and drive data-informed decision-making throughout the business.

Data Engineering and Innovation

Data engineers are pivotal in fostering business innovation and advancing technology by enabling organizations to harness their data. Strategic access to all types of business and operating data leads to more informed decision-making and helps to define competitive and innovative strategies. These, in turn, can translate into creative business outcomes.

Data engineers’ work developing advanced data pipelines and integrations also support implementing cutting-edge technology, from machine learning algorithms to predictive analytics. Data engineers create the foundation that allows for thorough data exploration, yes, by data scientists and analysts, but also through other operating interfaces that open up the world of data to a much larger, non-technical audience operating outside of these three roles.

And through these efforts, they bring a direct contribution to creative business outcomes, enabling organizations to reveal new perspectives and opportunities by:

  • Uncovering hidden trends and insights
  • Making accurate forecasts and decisions
  • Driving innovative and novel approaches
  • Optimizing existing and new operations

In essence, the work accomplished by data engineers is both strategic and foundational in converting data into actionable insights, which can drive innovation and business growth.

There’s also a direct connection between practical data engineering and a business’s capacity to innovate that mustn’t be ignored. Efficient data engineering practices enable smoother data flow and analysis, leading to faster decision-making that sticks, allowing the team to continue their journey of innovation as opposed to getting caught in re-decisions and rework.

To harness its full potential, businesses must examine the mechanics of data engineering, appreciating its intricacies and how they interplay with other facets of an organization. Our next post, “Fueling Business Excellence: Reimagining Business Success through the Lens of Data Engineering,” explores this subject in more detail. In it, we’ll look closer at how data engineers design, build, and maintain the critical infrastructure that enables detailed data analysis that can propel businesses forward.

  1. Data Engineer Demographics and Statistics in the US, Zippia, September 2022 (accessed June 6, 2022)
  2. Tech Salary Guide 2023, Motion Recruitment, November 2022
  3. Data Engineer Demographics and Statistics in the US, Zippia, September 2022 (accessed June 6, 2022)