Lead Analytics Engineer - Data Modeling & Quality

About this role

Arcadia is dedicated to happier, healthier days for all. We believe that there is a better healthcare world – one powered by data. Our platform transforms complex, diverse data into a unified foundation for health, helping organizations deliver better care, boost revenue, and lower costs. We’re a team of fiercely driven individuals committed to making healthcare more sustainable—and we’re looking for passionate people to help us get there. For more information, visit . Why This Role Is Important to Arcadia Arcadia 's data platform powers population health analytics for health plans, ACOs, and provider groups across the country. As a Lead Analytics Engineer — Data Modeling & Quality, you sit at the intersection of data quality ownership and analytical data modeling. You'll own the SQL and DBT layer that transforms raw clinical and claims data into trusted, production-grade datasets, while also serving as the quality authority for the data those models produce. This is a hybrid role — deeper SQL and DBT expertise than a traditional Data Health Professional, with a more analytical and model-focused scope than a Data Engineering role. You're less focused on pipeline infrastructure and more on the logic, shape, and trustworthiness of the data itself. What Success Looks Like In 3 months Independently triage and resolve pipeline data quality issues Author at least one new DBT model or refactor an existing one to meet current modeling standards Design a DBT test suite for a set of models lacking coverage Understand the end-to-end pipeline from ingress through silver and gold, and be able to trace a data quality issue to its root layer In 6 months Building strong working relationships with clients and cross-functional partners (Data Engineering, Customer Success) Deeply familiar with Arcadia 's full data stack — from ingress through silver, gold, and downstream consumers Driving at least one improvement project forward, whether technical (e.g. model refactor, new DQ framework) or process-focused (e.g. promotion playbook, triage workflow) In 12 months Recognized as a leader within the department — peers and stakeholders seek out your expertise on data modeling and quality Operating independently across the full scope of the role with minimal guidance Two or more improvement projects completed and in production, with measurable impact on data quality or operational efficiency 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 Hudi 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 score

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