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.* *For more information, visit**[](http://arcadia.io/)*. **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 - 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 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 ### What You'll Bring Education: - Bachelor's or Master's degree in Computer Science, Statistics, Business, Economics, or a related field Experience: - 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 Skills: - 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 ### Would Love For You To Have - 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 ### What You'll Get - Work alongside a talented team on some of the most complex and rewarding challenges in healthcare data - 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