Lead Analytics Engineer - Data Modeling & Quality

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.

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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