Role roadmaps / Data Engineer Role roadmap
Move data that other people can trust: ingest, transform, orchestrate, and keep the warehouse bill from becoming a surprise.
Also called: Analytics Platform Engineer, Pipeline Engineer
You ship: Pipelines with tests, an on-call story, and a warehouse someone else can query.
4–6 months to ship independently
Roadmap Coding Interview Question bank Switch roleData Analyst BI Analyst Data Engineer Analytics Engineer Data Scientist ML Engineer MLOps Engineer AI Engineer Data Architect Nearby roles:Analytics Engineer , Data Architect , MLOps Engineer
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Terminal Start · Station 1 of 18
Click to stop Start · Data Engineer Pipelines with tests, an on-call story, and a warehouse someone else can query. Inspect station brief →
Checkpoint · Station 2 of 18
Click to stop Foundations The tools you cannot fake in week one Inspect station brief →
Bus Stop · Station 3 of 18
Click to stop SQL as a production language Windows, anti-joins, late data. Not just SELECT for a dashboard. Open Syllabus →
Bus Stop · Station 4 of 18
Click to stop Python for jobs, not notebooks Packaging, retries, and memory. Pandas is a tool, not an identity. Open Syllabus →
Bus Stop · Station 5 of 18
Click to stop Warehouse modeling Medallion as a communication tool. Grain, SCD, and why gold is expensive. Open Syllabus →
Checkpoint · Station 6 of 18
Click to stop Daily craft What you actually do on a Tuesday Inspect station brief →
Bus Stop · Station 7 of 18
Click to stop Ingest without folklore Batch vs CDC, schema drift, and idempotent loads. Open Syllabus →
Bus Stop · Station 8 of 18
Click to stop Orchestration Airflow, Dagster, or Prefect — one clock, data-aware if you can. Open Syllabus →
Bus Stop · Station 9 of 18
Click to stop One warehouse, cost first Snowflake, BigQuery, or Databricks. Query Profile before Cortex. Open Syllabus →
Checkpoint · Station 10 of 18
Click to stop How you ship Production habits, not extra courses Inspect station brief →
Bus Stop · Station 11 of 18
Click to stop Tests that page someone Row counts, uniqueness, accepted values. Fail the job on grain. Open Syllabus →
Bus Stop · Station 12 of 18
Click to stop Streaming when batch is wrong Kafka, watermarks, and exactly-once as a contract, not a slogan. Open Syllabus →
Bus Stop · Station 13 of 18
Click to stop Open tables Iceberg or Delta, time travel, and compaction as an ops job. Open Syllabus →
Checkpoint · Station 14 of 18
Click to stop Career moves How this role talks in interviews Inspect station brief →
Bus Stop · Station 15 of 18
Click to stop The DE interview SQL, Python, a platform sketch, and an incident story. Open Syllabus →
Bus Stop · Station 16 of 18
Click to stop Certs as vocabulary SnowPro, Databricks, AWS — useful after you have shipped, not instead of. Open Syllabus →
Bus Stop · Station 17 of 18
Click to stop Next role Analytics engineer toward metrics. Architect toward platform. ML data toward features. Open Syllabus →
Final Destination · Station 18 of 18
Click to stop Keep practicing SQL + Python for this role Inspect station brief →
Start · Data Engineer Foundations SQL as a production language Windows, anti-joins, late data. Not just SELECT for a dashboard. Python for jobs, not notebooks Packaging, retries, and memory. Pandas is a tool, not an identity. Warehouse modeling Medallion as a communication tool. Grain, SCD, and why gold is expensive. Daily craft Ingest without folklore Batch vs CDC, schema drift, and idempotent loads. Orchestration Airflow, Dagster, or Prefect — one clock, data-aware if you can. One warehouse, cost first Snowflake, BigQuery, or Databricks. Query Profile before Cortex. How you ship Tests that page someone Row counts, uniqueness, accepted values. Fail the job on grain. Streaming when batch is wrong Kafka, watermarks, and exactly-once as a contract, not a slogan. Open tables Iceberg or Delta, time travel, and compaction as an ops job. Career moves The DE interview SQL, Python, a platform sketch, and an incident story. Certs as vocabulary SnowPro, Databricks, AWS — useful after you have shipped, not instead of. Next role Analytics engineer toward metrics. Architect toward platform. ML data toward features. Keep practicing Phase 1: Foundations SQL as a production language Windows, anti-joins, late data. Not just SELECT for a dashboard. Python for jobs, not notebooks Packaging, retries, and memory. Pandas is a tool, not an identity. Warehouse modeling Medallion as a communication tool. Grain, SCD, and why gold is expensive. Phase 2: Daily craft Ingest without folklore Batch vs CDC, schema drift, and idempotent loads. Orchestration Airflow, Dagster, or Prefect — one clock, data-aware if you can. One warehouse, cost first Snowflake, BigQuery, or Databricks. Query Profile before Cortex. Phase 3: How you ship Tests that page someone Row counts, uniqueness, accepted values. Fail the job on grain. Streaming when batch is wrong Kafka, watermarks, and exactly-once as a contract, not a slogan. Open tables Iceberg or Delta, time travel, and compaction as an ops job. Phase 4: Career moves The DE interview SQL, Python, a platform sketch, and an incident story. Certs as vocabulary SnowPro, Databricks, AWS — useful after you have shipped, not instead of. Next role Analytics engineer toward metrics. Architect toward platform. ML data toward features.
SQL as a production language Windows, anti-joins, late data. Not just SELECT for a dashboard.
DE interviews still open with SQL. The pad is not optional.
Checked automatically: you solved the linked practice problems.
SQL as a production language: windows, anti-joins, late data — not SELECT for a dashboard.
Time to learn 4–8 weeks of production SQL, not dashboard SQL.
Prerequisites Windows, anti-joins, late data as vocabulary A pad you will actually open What you need All SQL tracks on this site A warehouse (Snowflake / BigQuery / Databricks / Redshift — one is enough) dbt or a reviewed SQL repo Query Profile / EXPLAIN Concepts to study Window frames (ROWS vs RANGE) and running totals Anti-joins and reconciliation (orders vs payments) Idempotent loads: rerunning must not double revenue Late data and watermarks as a warehouse problem too QUALIFY / ROW_NUMBER for latest-per-key JSON/semi-structured in the warehouse Tech stacks Snowflake / BigQuery / Databricks SQL / Redshift — Production dialectdbt — How SQL gets reviewedThis site’s SQL tracks — The padHow to study Finish Warehouse SQL Core, then Windows, then Quality & Hard SQL. Rewrite one dashboard query as a tested dbt model. Practice talking through EXPLAIN / Query Profile. What to produce A CDC-into-gold sketch with where you dedup and what you page on A window query you can write from memory Practice on this site All SQL tracks; coding sandbox; DE interview SQL Pitfalls Notebook SQL that nobody can rerun Windows used as GROUP BY because the tutorial said so Ignoring late-arriving facts Interview prompts Design CDC into gold. Where do you dedup, and what do you page on? Links
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