The successful candidate will be responsible for building and maintaining data pipelines using dbt and Prefect to support data-driven decision-making across the organization.
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., S3, Glue, Lambda)
- Data quality and observability with dbt + Elementary
- Infrastructure-as-code with Terraform and version control with Git
- Dimensional data modeling (star/snowflake schemas, SCDs) and lakehouse concepts
- Strong problem-solving skills and clear communication with analysts, scientists, and stakeholders
Benefits & Perks
- Medical, dental, and vision benefits
- 15 days PTO/year
- 10 paid holidays
- Paid parental leave
- Personal phone bill reimbursement
- Gym reimbursement
- Corporate DoorDash® DashPass membership
- Regular company and team activities
- 401k with competitive matching contribution plan
- Excellent opportunities for career growth
- Work in a hyper-growth company
