Lead Analytics Engineer - Data Modeling & Quality

Arcadia

6d • $160,000 - $185,000 • Remote

About The Position

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.

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.

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

Nice To Haves

• 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

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 Hudi 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

Benefits

• Flexible, fully remote work environment with the resources and support to do your best work
• Exposure to senior leaders
• Be on the front lines of AI adoption — use cutting-edge tools to accelerate your work and shape how the team operates in an AI-first environment
• Make a meaningful impact on healthcare data operations by improving the quality, reliability, and trustworthiness of data that drives patient care decisions
• Be a part of a mission driven company that is transforming the healthcare industry
• Become a member of the talented, energized, diverse and purpose-driven Arcadian Community

What This Job Offers

• Job Type: Full-time
• Career Level: Senior