Senior Data Engineer

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