Data Engineer: Facts, Skills, and Next Paths

A Data Engineer builds and maintains the systems that collect, store, and process data so it can be used for analysis and decision-making. Day-to-day work involves designing data pipelines to move information from various sources into a central platform, writing code in Python or R, and using SQL to query and transform data. For example, at Wapa, a Data Engineer builds and maintains pipelines and models using SQL, Python, and cloud experience, working alongside other engineers and analysts.

Core responsibilities include developing and maintaining scalable data ingestion, transformation, and curation pipelines—often using tools like Databricks and AWS—to deliver analytics-ready datasets. The role sits within a data or analytics team, collaborating with analysts and engineers to centralize and modernize operational data. For instance, Ragle Inc. seeks a Data Engineer to design production-grade pipelines that connect in-house systems, accounting tools, and third-party APIs, powering daily analytics. At Accenture Federal Services, data engineers help the US federal government make the nation stronger and safer by pursuing the potential of technology.

The median salary for a Data Engineer is approximately $133,651. The most requested skills in current job postings are Data Integration (80%), Python or R (70%), SQL (70%), and Cloud Platforms (70%).