Senior Data Engineer

To design an end-to-end ETL pipeline with dbt and Airflow, you orchestrate transformation and testing workflows. The process ingests raw data, applies modeling strategies like Star schema, and validates quality before analytics.

The best way to enforce data quality checks in PySpark ELT workflows is to define grain, keys, and lineage explicitly. You configure validation gates that test transformed data in the gold schema before it reaches production analytics.

Yes, you can use this data engineering approach for Snowflake, BigQuery, and Redshift environments. It coordinates ETL and ELT projects across these platforms while enforcing consistent governance and idempotent pipeline execution.

To model Slowly Changing Dimensions and define grain in a data warehouse, you apply specific modeling strategies during transformation. You document the grain and keys for each model to ensure accurate lineage and data quality.

To build a repeatable ELT workflow that validates quality before analytics, you ingest raw data and transform it into a gold schema. You then apply data quality gates and document models and tests to ensure production readiness.

You need idempotent pipelines for data engineering orchestration to prevent duplicate records and ensure consistent outputs during reruns. Enforcing idempotency maintains robust data governance and reliable production-ready workflows.