The ability to design and operate pipelines that collect, transform, store, and serve data. A core competency for building the infrastructure that enables data-driven decision-making across organizations.
Data Engineering is the competency of collecting data from diverse sources, designing and implementing ETL/ELT pipelines, and building and operating data warehouses and data lakes. It encompasses schema design, data quality management, workflow orchestration, and real-time streaming processing, with the goal of providing reliable data infrastructure so that analysts and data scientists can access trustworthy data in a timely manner.
This is the entry point into data engineering, where you explore foundational concepts. You learn basic SQL syntax, understand pipeline concepts, and grasp the ETL (Extract, Transform, Load) workflow. You can identify relational database table structures and perform simple data extraction and transformation tasks with guidance.
What Comes Next
If you've checked off most of this list, you're ready for the Assist, Apply stage, building batch processing pipelines independently and validating data quality. Bandura(1977)'s Social Learning theory suggests watching experienced data engineers build pipelines and studying ETL case studies builds the confidence to tackle these tasks yourself.
Defines Data Engineering from Level 2 (Assist) to Level 6 (Initiate, influence), specifying pipeline design, implementation, and strategic responsibility scope at each level.
Details technical requirements, responsibility scope, and autonomy levels across Junior, Intermediate, Senior, Staff, and Principal stages for L1-L7 mapping.
Validates mid-to-senior engineer competency across 5 domains: data processing system design, ingestion/processing, storage, analysis readiness, and workload automation.
Defines 11 data management knowledge areas (governance, quality, metadata, etc.), providing authoritative grounding for L5-L6 governance/strategy checklists and L4 schema/quality management items.
Systematic mapping of 25 papers classifying data engineering lifecycle activities (collection, transformation, storage, serving) with technical solutions and architectures, grounding L3-L5 checklist behaviors.
