• Maintain and extend existing data mart builds: claims aggregation, financial metrics, clinical metrics, and curated analytical tables that power Innovu’s SaaS platform • Author, test, and maintain metric SQL templates against Innovu’s canonical data model (medical claims, pharmacy claims, eligibility, provider, plan design) • Build and maintain data pipelines in Python and SQL (Redshift, PostgreSQL, S3-based integrations) • Profile and validate third-party data feeds during vendor onboarding (new data sources land regularly) • Investigate data quality issues from warehouse transformation logic through final output • Produce analytical deliverables for clients and internal stakeholders (PMPM trending, cohort analysis, cost drivers) • Write clear documentation for metric definitions, data lineage, assumptions, and known limitations • Participate in monthly rotation for support triage (incoming tickets), release management, and mart build monitoring • Participate in code reviews and contribute to team standards

• Build, maintain, refactor and optimize data models in our dbt project • Serve as the architect for our dbt project as a whole • Create a consistent user experience across our warehouse and BI tools • Mentor and uplevel analysts and data engineers in data modeling and dbt best practices • Partner with the Data Engineering team to manage our data stack • Be a champion of data privacy and quality

• Analyze data access needs in collaboration with business users, data analysts and dataviz engineers • Implement data pipelines and data models using modern data stacks (SQL, dbt, ELT tools, GCP/Azure/AWS/Snowflake, …) • Expose data in BI and visualization tools • Maintain pipelines and implement alerts & data quality tests • Build a Customer Data Platform for a major retailer (8 brands / 30+ countries) • Migrate a large dashboard infrastructure from a Supermetrics-based stack to an Adverity + BigQuery stack • Implement end-to-end operational reporting tools for several high-growth startups • Package internal tools for custom attribution

• Design, build, and maintain analytics-ready data models that power dashboards, reports, and advanced analysis. • Develop and maintain dbt projects, ensuring high-quality data models, tests, and documentation. • Build and orchestrate data pipelines using Dagster or similar orchestration frameworks. • Implement data quality checks and monitoring to ensure accuracy and consistency. • Optimize pipelines and data models for performance, scalability, and cost efficiency. • Collaborate with Marketing, Operations, and Analytics teams to define metrics, KPIs, and reporting needs. • Maintain clear documentation of data models, metrics definitions, and pipeline logic. • Work closely with IT and data platform teams to align with governance, security, and engineering standards.