Skills for
Data Engineers

Direct data-engineer role for ETL, CDC, quality, and batch/stream processing.

Direct data-engineer role covering pipelines, warehouses, streaming, quality, and governance.

Direct implementation-focused data-engineering skill for reliable production pipelines.

Direct data-engineer role covering modern data platforms, ETL, streaming, and warehousing.

Direct data-engineer role for ETL/ELT, warehouses, streaming, orchestration, and data quality.

Direct data-engineering agent for ETL, warehousing, streaming, modeling, and quality.

Direct data-engineer role for pipelines, warehouses, Spark, Airflow, Kafka, and quality.

Direct data-engineer agent for ETL, warehouses, streaming, and analytics infrastructure.

Direct data-engineer role for scalable pipeline and data-platform delivery.

Direct data-engineer agent for reliable pipelines, lakehouses, streaming, and data quality.

Direct data-engineering role for ingestion, cleaning, schemas, quality, and pipelines.

Engineering-skill index includes a dedicated senior data engineer skill but is not itself the specialist.

Relevant to privacy controls in data pipelines, but primarily a Java/GDPR compliance skill.

Relevant to data-platform interoperability and data-sharing controls, but primarily legal/compliance engineering.

Includes data pipeline concerns for LLM systems but is primarily LLM engineering.

Supports role expectations and career progression, potentially useful to data engineers but not a practice skill.

Contains a data-engineer learning path but broadly combines data engineering, ML, AI, and MLOps.

Includes data-pipeline architecture on AWS but is primarily general cloud architecture.

Includes Azure data-pipeline architecture but is primarily general cloud architecture.

Strong adjacent specialty for data-quality work, but narrower than the data-engineer role.

Useful adjacent database-design capability, but not data engineering broadly.

Useful for data modeling and schemas, but primarily application database design.

Includes serverless data-pipeline patterns but is primarily general GCP architecture.

Can create data-engineer hiring loops, but is not a data-engineer execution skill.

Useful for data and database migrations, but broader migration planning is its primary scope.

Useful for pipeline monitoring and operational telemetry, but not data-engineer specific.

Can support Spark/SQL-related performance work indirectly, but is general application profiling.

Relevant to retrieval data pipelines but focused on RAG architecture rather than general data engineering.

General architecture skill can support data-platform decisions but is not role-specific.

Adjacent MLOps and feature-platform work, but primarily model productionization.

Useful for reliability targets of production data pipelines, but an SRE specialty.

Strong specialized data-warehouse and pipeline skill, limited to Snowflake.

Useful SQL and migration specialty, but narrower and more database/application focused.

Useful for evaluating data-platform technologies, but not a data-engineer specialist.

Builds extraction and validation pipelines, but web scraping is narrower than data engineering.