Data Quality Engineer

Crossover Health

1d•Remote

About The Position

Crossover Health is creating the future of health as it should be. A national, team-based medical group with a focus on wellbeing and prevention that extends beyond traditional sick care, the company delivers an entirely new model of healthcare—Primary Health—built on the foundation of trusted relationships, an interdisciplinary care team approach, and outcomes-based payment. Crossover’s Primary Health model integrates primary care, physical medicine, mental health, health coaching, care navigation and more, and delivers care in surround-sound—in-person, virtually and via asynchronous messaging. Together we are building a community of members that embraces healthcare as a proactive part of their lifestyle. Job Summary Crossover Health is seeking a highly skilled and experienced Data Quality Engineer to strengthen our data foundations across the enterprise. This role blends technical QA and data stewardship,, helping ensure that our data is trusted, tested, and well-documented at every stage of its lifecycle. You will work closely with Data Engineering, Data Science, Analytics, Product, Security, and Operations teams to document, test, and ensure completeness and accuracy of our data pipelines, and drive end-to-end transparency in how our data flows across systems. This is a hands-on role for someone who can dive into code, build and maintain automated data quality coverage, and drive issue triage and resolution. This role plays a critical part in ensuring the trustworthiness of the data used across clinical operations, reporting, analytics, and data delivery - supporting compliance, operational efficiency, and client satisfaction.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Data Science, or related field
4+ years of experience in a data quality, data engineering, analytics engineering, or data governance role
Strong knowledge of QA methodologies and data testing frameworks (e.g., dbt tests, Elementary, Great Expectations)
Proficiency in: SQL and dbt (including documentation, testing, and modeling)
Python for scripting, QA automation, or monitoring workflows
Data pipelines orchestration tools (e.g., Airflow, Fivetran)
Snowflake
Proficiency in writing clear, concise, and comprehensive test plans, test cases, and documentation
Ability to identify and resolve data issues across complex systems with minimal supervision
Excellent problem-solving skills and attention to detail.
Strong communication and teamwork skills.
Ability to work independently and manage multiple priorities in a fast-paced environment.

Nice To Haves

Experience working with healthcare datasets
Familiarity with Master Data Management tools, data mastering concepts, and data enrichment workflows
Experience with core data governance concepts
Understanding of data privacy, access controls, and compliance standards (e.g., HIPAA, PII/PHI)
Familiarity with BI tools such as Tableau for reviewing and validating data outputs

Responsibilities

Own the QA strategy for data pipelines, ensuring accuracy, completeness, and reliability of healthcare and operational data.
Maintain comprehensive documentation of data models, lineage, and critical fields in core tools like Snowflake, dbt, and Tableau.
Map data lineage from source systems (e.g., EMR, eligibility, claims) through modeling and reporting, ensuring visibility across the entire data lifecycle.
Design and implement data quality tests using dbt tests, Elementary, or similar QA tools.
Expand automated data QA test coverage across systems with limited or no existing validation.
Identify and validate data integrity issues across key sources such as claims, EMR, client files, and internal systems, assess impact, and coordinate resolution with cross-functional teams.
Maintain an issue tracker and regularly report on open items and progress to stakeholders.
Act as a liaison across Data Engineering, Data Science and Analytics, Product, Security, and Operations to support and advocate for data quality.
Support root cause analysis by debugging test coverage or validation scripts .
Partner with Data Engineering to recommend improvements to data models and pipelines based on QA findings.
Support the onboarding of new data sources by defining QA and documentation requirements in partnership with data engineering.
Performs other duties as assigned