Senior Data Engineer

Generate pipeline configurations (Airflow, Prefect, Dagster), validate data quality with profiling and anomaly detection, and optimize SQL/Spark performance with actionable recommendations.

Core Capabilities

• Pipeline generation — Airflow/Prefect/Dagster DAG code for batch and incremental loads, with DAG validation.
• Data quality — schema validation, profiling, anomaly detection, data contracts, and Great Expectations suite generation.
• ETL/ELT optimization — SQL and Spark analysis, partition strategy, and query cost estimation per warehouse.
• Architecture decisions — batch vs streaming and warehouse vs lakehouse trade-off frameworks.
• Reliability patterns — incremental watermarks, dead letter queues, freshness checks, and schema-drift detection.

When to Use

• Designing a data architecture or choosing batch vs streaming / warehouse vs lakehouse.
• Building or generating Airflow/Spark/dbt pipelines.
• Adding data-quality checks or data contracts.
• Optimizing slow ETL/ELT queries or troubleshooting pipeline failures.

Clarify First

Before generating pipelines, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• Orchestrator — Airflow / Prefect / Dagster (--type; changes the generated DAG code)
• Source, destination & load mode — systems involved and batch vs incremental (--source/--destination/--mode; shapes the pipeline)
• Data-quality expectations — the schema and contracts to enforce (drives the Great Expectations suite generation)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Quick Start

# Generate an Airflow DAG for incremental PostgreSQL -> Snowflake
python scripts/pipeline_orchestrator.py generate \
  --type airflow --source postgres --destination snowflake \
  --tables orders,customers --mode incremental --schedule "0 5 * * *"

# Validate data quality against a schema
python scripts/data_quality_validator.py validate data.csv \
  --schema schema.json --detect-anomalies --json

# Profile a dataset
python scripts/data_quality_validator.py profile data.csv --json

# Optimize a slow SQL query
python scripts/etl_performance_optimizer.py analyze-sql query.sql \
  --warehouse snowflake --json

# Estimate query cost
python scripts/etl_performance_optimizer.py estimate-cost query.sql \
  --warehouse bigquery --stats data_stats.json --json

Tools

All subcommands support --json for machine-readable output and --output for file writing.

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

• references/pipeline-workflows.md — the three end-to-end worked pipelines with code: batch ETL (PostgreSQL → dbt → Snowflake), real-time streaming (Kafka → Spark → Delta Lake), and the data-quality framework. Read when building a concrete pipeline.
• references/decisions-and-troubleshooting.md — the batch-vs-streaming and warehouse-vs-lakehouse decision frameworks, anti-patterns, and the troubleshooting table. Read when choosing an architecture or diagnosing a failure.
• references/data_pipeline_architecture.md — deep reference on pipeline architecture patterns. Read for architecture design depth.
• references/data_modeling_patterns.md — dimensional modeling and data-modeling patterns. Read when modeling marts and dimensions.
• references/dataops_best_practices.md — DataOps practices for CI/CD, testing, and operating pipelines. Read when operationalizing pipelines.

Integration Points