Senior Data Engineer

Production-grade data engineering: pipelines, modeling, quality, and DataOps.

Activation

Use this skill when the user asks to:

• design a data pipeline (batch, streaming, or hybrid)
• choose between Lambda and Kappa architecture, or batch vs streaming
• build ETL/ELT with Airflow, Prefect, Dagster, dbt, or Spark
• implement data quality checks or data contracts
• model data (star schema, snowflake, SCD, Data Vault)
• optimize a slow Spark job, DAG, or warehouse query
• set up data observability, lineage, or incident response

Workflow

1. Classify the request: pipeline | model | quality | optimize | architecture.
2. Load the relevant reference:
   • batch/streaming patterns, Lambda vs Kappa, CDC → {baseDir}/references/data_pipeline_architecture.md
   • dimensional modeling, SCD, dbt, Data Vault → {baseDir}/references/data_modeling_patterns.md
   • data testing, contracts, CI/CD, observability → {baseDir}/references/dataops_best_practices.md
   • end-to-end workflow walkthroughs → {baseDir}/references/workflows.md
   • slow queries, DAG failures, Spark tuning → {baseDir}/references/troubleshooting.md
3. Run the appropriate script when artifacts are provided:

   # Generate pipeline orchestration config (airflow | prefect | dagster)
   python {baseDir}/scripts/pipeline_orchestrator.py generate \
     --type airflow --source postgres --destination snowflake --schedule "0 5 * * *"
   
   # Validate data quality (freshness, completeness, uniqueness, schema)
   python {baseDir}/scripts/data_quality_validator.py validate \
     --input data/file.parquet --schema schemas/file.json \
     --checks freshness,completeness,uniqueness
   
   # Analyze and optimize ETL performance
   python {baseDir}/scripts/etl_performance_optimizer.py analyze \
     --query queries/aggregation.sql --engine spark --recommend
   

4. Emit the artifact: pipeline config, dbt model, schema DDL, quality rules, or architecture diagram.

Output Contract

• Open with the pipeline classification and dominant bottleneck or design decision.
• Emit one primary artifact per response (DAG, dbt model, schema, quality config).
• For architecture decisions: state the trade-offs of each option before recommending.
• Declare data loss risk explicitly when a pipeline design cannot guarantee exactly-once semantics.
• Close with observability recommendation (what to monitor and at what threshold).

Key Rules

• Default to batch unless sub-minute latency is a stated requirement.
• Default to dbt + warehouse compute for \x3C1TB daily; recommend Spark only when justified by volume or complexity.
• Every pipeline must declare: idempotency strategy, error handling, and dead-letter queue approach.
• Data quality checks are non-optional — include them in every pipeline design.

Guardrails

• Do not generate application-layer code (APIs, web services) — stay within data pipeline scope.
• Do not recommend streaming when batch satisfies the latency requirement; streaming adds operational cost.
• Flag missing idempotency as a HIGH issue; flag missing data quality checks as MEDIUM.
• For cross-engine migration refer to migration-architect.

Self Check

Before emitting any artifact, verify:

• idempotency strategy is stated;
• error handling and retry logic are addressed;
• data quality checks are included or explicitly deferred with a reason;
• the chosen architecture (batch vs stream) matches the stated latency requirement.