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Data Engineer

Build the pipes data flows through: reliable pipelines, clean models, and warehouses analysts and ML can trust.

✦ Guide me on this path with AI

Roadmap progress

0% 0 of 7 steps done

How to read the signals

Importance High Market demand Medium Automation risk Low
  1. 1

    Advanced SQL

    CTEs, window functions, query optimization. The data engineer's native tongue.

    Importance High Market demand High Automation risk Low
  2. 2

    Python for pipelines

    Batch jobs, APIs, file formats (Parquet). Glue that moves data reliably.

    Importance High Market demand High Automation risk Medium
  3. 3

    Warehouses & lakehouses

    BigQuery/Snowflake/Databricks. Where analytics data lands and scales.

    Importance High Market demand High Automation risk Low
  4. 4

    ETL/ELT & orchestration

    Airflow/dbt, scheduling, dependencies, retries, idempotency.

    Importance High Market demand High Automation risk Medium
  5. 5

    Data modeling

    Star schemas, normalization, slowly-changing dimensions. Clean models scale.

    Importance High Market demand Medium Automation risk Low
  6. 6

    Streaming

    Kafka, event-driven pipelines, exactly-once. Real time when the business needs it.

    Importance Medium Market demand High Automation risk Low
  7. 7

    Quality & governance

    Tests, lineage, cataloging, access control. Trust is the real deliverable.

    Importance High Market demand High Automation risk Low