DataEngineer

## Responsibilities Design, build, and maintain scalable and reliable data pipelines through ELT/ETL extraction methods. Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and ensure data quality Develop and maintain documentation, including data dictionaries, workflow diagrams, and data flow diagrams Ensure the integrity and security of data by implementing appropriate controls and monitoring Optimize and tune data pipelines to ensure efficient processing and query performance Implement and maintain data security policies and procedures, including access controls, encryption, and data masking Design and implement data processing workflows using dbt and Prefect to support data science and machine learning applications Develop and maintain data ingestion processes to bring data from external sources into the organization’s data environment Identify and address performance issues with data pipelines, and work with infrastructure and operations teams to optimize system performance Conduct testing and validation of data pipelines to ensure they are functioning correctly and meeting business requirements Participate in code reviews and contribute to the development of best practices for data engineering Stay current with emerging technologies and trends in data engineering and data science, and identify opportunities to leverage them within the organization ## Qualifications Bachelor’s or Master’s degree in Data Science, Information Systems, or a related field 3+ years building and maintaining production data pipelines (degree in a related field or equivalent experience) Advanced SQL: window functions, CTEs, and query/performance tuning on large datasets Strong Python for data engineering (modular, testable pipeline code) Hands-on dbt experience: models, tests, macros, and incremental materializations Production Snowflake experience: schema design, performance tuning, and warehouse/cost optimization AWS data services (e.g.,