senior-data-engineer

To build a scalable ETL pipeline using Airflow and dbt, you orchestrate batch data extraction with Airflow and apply SQL-based transformations in dbt. This approach separates pipeline scheduling from data modeling, resulting in reliable, maintainable data workflows.

Implementing data quality checks in a data pipeline requires setting up frameworks for validation, monitoring, and anomaly detection. By integrating these checks directly into your ETL processes, you ensure data reliability and catch anomalies before they reach the data warehouse.

Designing a batch ETL process to load data into Snowflake involves ingesting source data, transforming it using a tool like dbt, and loading it into the warehouse. This architecture ensures data is structured, accessible, and performant for downstream analytics.

Yes, Spark is frequently used for real-time streaming data pipelines alongside tools like Kafka. By architecting streaming processes with Spark, you process continuous data flows efficiently, enabling real-time data infrastructure and immediate analytics.

Yes, this data engineering approach explicitly supports implementing dimensional models, data vaults, and optimized schemas. By applying these data modeling techniques, you structure raw data into accessible formats that accelerate analytical querying and reporting.

DataOps principles are needed for data infrastructure to automate pipeline monitoring, enforce data governance, and troubleshoot data issues efficiently. Applying DataOps ensures your data systems remain scalable, reliable, and performant as organizational data volume grows.