What You'll Be Doing
DATA MODELING & DBT DEVELOPMENT
Author, review, and maintain DBT models using Spark/Hudi from ingest through bronze and silver
Help clients understand their data model, assumptions, and limitations through intentional validation
Troubleshoot and fix issues, then write DBT tests to catch issues proactively
Optimize SQL performance for slow-running jobs
Partner with Data Engineering on table design, partition strategy, and incremental patterns
DATA QUALITY OWNERSHIP
Triage and classify data quality alerts, distinguishing source-level issues from transform-layer failures
Design and maintain volume monitors and DQ monitors (null rate, distribution, future-date checks)
Author and apply clinical DQ rules (entity volume, field coverage, LOINC coverage, referential integrity) and claims validation rules across silver and gold layers
Conduct quality reviews for connector promotions — evaluating silver entity coverage, validation rule pass rates, and bronze-to-silver transformation correctness
Own the ticket queue for DQ, attribution, hierarchy, and customer-specific data quality issues, writing clear customer-facing findings
CROSS-FUNCTIONAL QUALITY COLLABORATION
Lead data quality reviews during connector installation and promotion (UAT → PRD), including claims validation playbooks and null analysis
Partner with Data Engineering on root-cause triage for errors, ingress anomalies, and silver table issues surfaced through data quality monitoring
Coordinate with the Measure Implementation Team (MIT) when data quality issues affect quality measure scores
Contribute to and enforce data modeling standards across teams
TECHNOLOGIES
Data modeling: DBT-Spark, SQL, Claude
Warehousing: Amazon Redshift, Apache Hudi, AWS Athena
Data quality: volume/DQ monitors, DBT tests
Orchestration: Argo Workflows, Airflow
Source control: Git / GitHub, PR-based review workflows
Observability: Grafana, Jira
- Healthcare data: Claims (plan/professional/pharmacy), EHR (clinical entities), MPI
