Quick overview
Job description
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.
- 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
- 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)
- 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
- 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
- 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
- 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 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
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