senior-data-engineer

To architect scalable data pipelines, you plan end-to-end data architectures using Python, SQL, Spark, and Airflow, applying data modeling, orchestration, and quality checks to ensure robust ingestion and governance across large-scale data platforms.

The best way to orchestrate ELT processes with Airflow and dbt is to design DataOps workflows that integrate pipeline orchestration, data quality guardrails, and lineage tracking, ensuring production-grade data transformations and reliable execution.

Yes, you can design data governance and quality checks for Kafka streams by applying enterprise-grade data architecture frameworks that define modeling, lineage, and quality guardrails across diverse ingestion scenarios.

You implement DataOps guardrails for Spark data engineering by establishing defined decision frameworks that enforce production-grade requirements, including data quality checks, orchestration rules, and governance policies throughout the pipeline lifecycle.

You need to plan enterprise data architecture when struggling to operate scalable data pipelines across rapidly growing data volumes, requiring structured DataOps, data modeling, and orchestration to manage diverse tech stacks effectively.