senior-data-engineer

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---
name: senior-data-engineer
description: >
  Data engineering for batch and streaming pipelines with Airflow, dbt, Spark, and Kafka. Use
  when designing data architectures, building pipelines, adding data-quality checks,
  optimizing ETL/ELT, or troubleshooting pipeline failures.
license: MIT + Commons Clause
metadata:
  version: 1.2.0
  author: borghei
  category: engineering
  domain: data-engineering
  updated: 2026-06-17
  tags: [airflow, spark, data-pipelines, warehousing, etl]
  python-tools: pipeline_orchestrator.py, data_quality_validator.py, etl_performance_optimizer.py
  tech-stack: python, sql, spark, airflow, dbt, kafka
---
# 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

```bash
# 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

| Tool | Subcommands | Purpose |
|------|-------------|---------|
| `pipeline_orchestrator.py` | `generate`, `validate`, `template` | Generate Airflow/Prefect/Dagster pipeline code, validate DAGs |
| `data_quality_validator.py` | `validate`, `profile`, `generate-suite`, `contract`, `schema` | Schema validation, profiling, anomaly detection, Great Expectations |
| `etl_performance_optimizer.py` | `analyze-sql`, `analyze-spark`, `optimize-partition`, `estimate-cost`, `template` | SQL/Spark optimization, partition strategy, cost estimation |

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](references/pipeline-workflows.md)**