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 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

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

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

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

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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