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Sr. Analytics Engineer

Job in Austin, Travis County, Texas, 73301, USA
Listing for: Privia Health
Full Time position
Listed on 2026-10-28
Job specializations:
  • IT/Tech
    Data Analyst, Data Engineering, Data Warehousing
Salary/Wage Range or Industry Benchmark: 102000 - 120000 USD Yearly USD 102000.00 120000.00 YEAR
Job Description & How to Apply Below

Privia Health™ is a national physician platform transforming the healthcare delivery experience. We provide tailored solutions for physicians and providers, creating value and securing their future. Through high-performance physician groups, accountable care organizations, and population health management programs, Privia works in partnership with health plans, health systems, and employers to better align reimbursements to quality and outcomes.

As Privia Health continues to expand, the Analytics team requires an agile and tech-savvy Healthcare Analytics Engineer to bridge the gap between complex raw healthcare data and strategic business operations. In this role, you will design, model, and maintain robust analytics infrastructure by combining advanced SQL practices, BI tooling, and analytics engineering principles. You will sit at the intersection of payer claims (medical, pharmacy, attribution), Electronic Medical Record (EMR/EHR) clinical data, and operational feeds, transforming disparate sources into clean, centralized analytical datasets.

Operating in a fast-paced environment, you will empower operational teams, clinical leaders, and business stakeholders with trustworthy insights, rapid ad-hoc query support, and scalable data models that support broad healthcare initiatives—including Value-Based Care (VBC) performance, clinical workflow optimization, and enterprise reporting.

Primary

Job Duties
  • Design, build, and maintain production-ready data models and semantic layers using advanced SQL and analytics engineering best practices to support enterprise reporting and self-service analytics.

  • Ingest and blend diverse healthcare datasets—including payer claims, attribution rosters, quality measure extracts, EMR/clinical data, and operational logs.

  • Execute fast-turnaround ad-hoc SQL queries and deep-dive analyses to answer pressing operational, clinical, and financial questions across various business units.

  • Define and standardize core business metrics across operational domains, including practice operations, provider utilization, clinical quality measures, and VBC performance tracking.

  • Partner closely with cross-functional stakeholders (IT, Operations, Clinical Support, Payer Relations) to gather requirements, translate operational needs into technical logic, and iterate quickly in an agile environment.

  • Conduct automated data validation, quality assurance testing, and performance tuning on data warehouse queries and transformation jobs to maintain data integrity.

  • Serve as an internal SME on data structures, clinical workflows, and payer data layouts, assisting non-technical teams in interpreting underlying data accurately.

  • Document data models, logic definitions, and data flow pipelines and collaborate with BI teams to ensure transparency and consistency across the enterprise analytics landscape.

  • Other duties as assigned.

  • Bachelor’s degree in Healthcare Analytics, Data Science, Computer Science, Health Informatics, Information Systems, or a related quantitative field preferred, or equivalent practical experience.

  • 3+years of professional experience in Healthcare Analytics, Analytics Engineering, or Business Intelligence within a provider group, health system, payer, or healthcare technology environment.

  • Strong domain experience working with both payer claims data (medical, pharmacy, eligibility/attribution) and EMR/clinical data (e.g., Athenahealth, Epic, Cerner).

  • Advanced SQL skills:
    Expert-level ability to write complex queries, multi-table JOINs, CTEs, window functions, query optimization, and data transformation routines.

  • Exposure to modern analytics engineering workflows and tools (e.g., dbt, Python/Pandas, Git/version control, cloud data warehouses like Snowflake, MS SQL Server, or Big…

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