- Engage stakeholders directly to gather, clarify, and document project requirements.
- Translate requirements into architected data solutions: choose the right storage, pipeline, modeling, and delivery approach for each problem.
- Own testing end-to-end — unit tests, data quality checks, reconciliation, and integration tests before anything reaches production.
- Deploy solutions to production and monitor post-deployment health, iterating rapidly based on real-world feedback.
- Run parallel AI coding sessions (Claude Code, Cursor, Codex) across different facets of a pipeline simultaneously — orchestrate, verify, and integrate the outputs.
- Design and build complex, reliable data pipelines ingesting from AWS, Azure, Salesforce, MuleSoft, and multiple third-party APIs into our AWS Data Lake and Snowflake warehouse.
- Implement and evolve data models using Kimball methodology to support financial, operational, and clinical analytics.
- Build and support Power BI data models and reports; empower analytics team members to self-serve on a reliable data foundation.
- Build lightweight internal data applications and tooling where needed; data entry interfaces, operational dashboards, automation scripts that bridge the gap between data pipelines and end users.
- Ensure data security and HIPAA compliance in all pipeline and application work. Partner with IT to enforce data governance standards.
GraphQLNode.jsPython+9 more