About the company
Shape a brighter financial future with us. Together with our members, we’re changing the way people think about and interact with personal finance. We’re a next-generation financial services company and national bank using innovative, mobile-first technology to help our millions of members reach their goals. The industry is going through an unprecedented transformation, and we’re at the forefront. We’re proud to come to work every day knowing that what we do has a direct impact on people’s lives, with our core values guiding us every step of the way. Join us to invest in yourself, your career, and the financial world.
Responsibilities
- Develop and maintain robust data pipelines and datasets.
- Build foundational data products for key business areas.
- Enhance self-service data capabilities for the People Team.
- Ensure high standards in ETL/ELT operations, data quality, and pipeline reliability.
- Design and build production dbt models in Snowflake that integrate Workday and other People systems into well-modeled, documented datasets, including slowly changing dimensions for People history.
- Build and operate Airflow DAGs that ingest People systems data and orchestrate dbt runs, keeping loads reliable and re-runnable.
- Own data quality and observability: dbt tests, freshness checks, row-count validation, and monitoring so issues are caught before stakeholders see them.
- Implement and maintain row-level security and access controls for People data, partnering with governance and security teams.
- Build and maintain semantic models and AI agents, held to the same modeling, access, and quality standards as our pipelines.
- Collaborate with cross-functional teams, such as other data engineers, people analysts, data scientists, and business stakeholders, to translate requirements into production-ready deliverables, and communicate technical trade-offs to non-technical partners.
- Enforce data governance policies and practices to maintain data integrity, security, and compliance with relevant regulations.
- Mentor other engineers through code review, modeling standards, and technical guidance.
Requirements
- A bachelor's degree in Computer Science, Data Science, Engineering, or a related field.
- 5+ years of experience in data engineering and analytics technical strategy.
- Proficiency in data engineering tech stack: Python / SQL / dbt / Airflow / GitLab.
- Proficiency in relational and cloud database platforms such as Snowflake, Redshift, or GCP.
- Thorough knowledge of data modeling, database design, data architecture principles, data operations, and CI/CD.
- Strong analytical and problem-solving abilities, with the capability to simplify complex issues into actionable plans.
- Experience with sensitive or regulated data and access controls.
- Experience with AI agents or LLM-assisted analytics, and with the HR / People function, is advantageous.