Senior Data Engineer
About the Position
We are looking for a hands-on Data Engineer who can own data work end to end, from understanding source systems and building ingestion and transformation pipelines to delivering clean, well-modeled, production-ready datasets for downstream consumers. We are seeking a data-first professional who is equally comfortable writing SQL and dbt transformations, building and orchestrating Python/Airflow pipelines, investigating data issues deep within the stack, and designing data models that will stand the test of time.
Responsibilities
- Build, maintain, and enhance data pipelines and transformation workflows.
- Develop and orchestrate ingestion and processing pipelines in Python and Airflow.
- Develop clean, reliable, and reusable data models to serve downstream analytics and application consumers.
- Work with SQL and Snowflake to investigate data, troubleshoot issues, and implement transformations.
- Translate business and downstream requirements into appropriate data models and pipeline designs.
- Perform data analysis, validation, reconciliation, and data quality checks.
- Troubleshoot and resolve data issues across the pipeline, from source systems through ingestion and transformation to the serving layer.
- Optimize Snowflake performance and cost, including query tuning, warehouse sizing, and clustering and partitioning decisions.
- Take ownership of assigned tasks and independently drive them through development, testing, and completion.
- Work closely with business, analytics, and engineering teams to deliver production-ready solutions.
Requirements
- Strong SQL and data analysis skills.
- Strong hands-on experience with Snowflake.
- Hands-on experience with dbt and modern ELT and data transformation practices.
- Strong experience with Python and Airflow.
- Solid understanding of data modeling, including dimensional modeling and fact and dimension structures.
- Strong experience with AWS cloud services.
- Ability to understand unfamiliar datasets and identify relationships, data quality issues, and transformation requirements.
- Experience debugging and supporting existing data pipelines in production.
- Comfortable working independently in a fast-paced environment and delivering tasks with limited supervision.
