Lead Analytics Engineer - Data Modeling & Quality

Arcadia provides a healthcare data platform that transforms complex clinical and claims data into a unified foundation for population health analytics. The Lead Analytics Engineer will own DBT-based data modeling and data quality processes, develop trusted production datasets, troubleshoot issues, and collaborate with engineering and cross-functional teams to improve healthcare data reliability.

Responsibilities:

• 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
• 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
• 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

Requirements:

• • Bachelor's or Master's degree in Computer Science, Statistics, Business, Economics, or a related field
• • Advanced SQL: window functions, complex CTEs, aggregation patterns, performance tuning on columnar databases
• • DBT: hands-on experience authoring models, tests, macros, and yml documentation; familiarity with incremental strategies
• • Healthcare data literacy: working knowledge of claims data (professional, institutional, pharmacy), clinical data (EHR entities), and common quality dimensions (member months, coverage rates, null patterns)
• • Data quality mindset: ability to differentiate source data issues from transform issues, design systematic validation checks, and communicate data quality findings clearly
• • Clear communicator — able to translate technical findings for clients and non-technical stakeholders
• • Strong analytical judgment — you can look at a distribution and know when something is wrong
• • Ability to manage several projects simultaneously, leveraging AI tooling to stay organized and efficient
• • Genuine desire to learn and apply AI tools for operational efficiency
• • Experience with Spark SQL and Hudi table format
• • Familiarity with data quality monitoring tools
• • Comfortable operating in an AI-first environment using Claude to build/verify various day-to-day workflows
• • Exposure to population health analytics concepts: HEDIS measures, risk adjustment, value-based care metrics
• • Python scripting for data investigation and automation
• • Experience with Argo Workflows or similar orchestration platforms
• • Healthcare data standards: ICD-10, CPT, NDC, LOINC, NPI