Senior Data Engineer - Analytics

Description

The company is a healthcare intelligence business that serves health plans across the US. Its platform is used by more than 85 health plans, including many of the top 20, covering over 270 million lives.

This senior role owns the layer where messy operational data becomes figures that leaders act on. You will work closely with finance, operations, and client-facing staff, learn how the work runs day to day, and produce certified models, metric definitions that hold up under review, and reports people rely on.

The analytics warehouse merges 4 platforms into one measurement model covering dozens of distinct client workflows.

Modeling and measurement matter more here than pipeline building, though pipeline skills help. Expect a lot of time on calls with business teams and in dbt, SQL, and Power BI. You should be comfortable not shipping a dashboard you cannot reconcile.

Responsibilities

• Hold discovery sessions with business stakeholders to uncover the real need behind a request, then turn it into acceptance criteria you can build against.
• Build certified dimensional models in dbt and SQL on Databricks across bronze, silver, and gold layers. Source systems often conflict in subtle ways, and resolving those conflicts is the core of the work.
• Define each metric once, in one governed place, written so a non-engineer can follow it. Today one operating metric is derived in 12 models, and consolidating that into a governed semantic layer is your job.
• Build and maintain the visualization layer: Power BI semantic models, dashboards, and paginated reports, including refresh health and how current each semantic model is. Choose the right chart for each question, make hierarchies readable at a glance, and be able to justify each choice.
• Monitor your models for freshness, latency, statistical anomalies, and value drift. Add data tests, regression guards, and CI checks so fixed defects stay fixed.
• Design and run orchestration for what you build: dbt Cloud jobs, Airflow DAGs, scheduling, dependency order, and reliable incremental runs.
• Document designs, models, and the CI-enforced knowledge base. Write handoff packets a business reader can use, so the next owner does not have to rebuild the logic.
• Turn recurring work into repeatable, tested, AI-assisted workflows. Direct LLMs and check what they produce.

Requirements

• 6+ years in analytics engineering, BI engineering, data engineering, or business analysis, including production ownership of the models and metrics you built.
• Advanced SQL and solid dimensional modeling. You catch a grain mismatch in another person's model before you run it.
• Production dbt experience covering models, tests, macros, exposures, documentation, and CI, with clear views on project structure that you can defend.
• Hands-on work with a cloud lakehouse or warehouse. Databricks and Spark SQL preferred; Snowflake, BigQuery, or Redshift also transfer well.
• Strong visualization skill. You have owned a BI semantic layer and the reports built on it, not only the underlying tables. Power BI and DAX strongly preferred; Looker/LookML, Tableau, MetricFlow, or Cube are credible alternatives. Bring dashboards you designed and be ready to explain what you left out.
• Stakeholder skills: run requirements conversations with non-technical owners, turn vague complaints into testable definitions, describe data problems as business risk, and tell someone their metric is wrong without losing them.
• When two numbers disagree, you investigate the cause rather than averaging them, escalating them, or trusting the newer one.
• Routine use of Git and pull requests.
• Python for API calls, file parsing, statistics, and report generation, plus the judgment to use SQL where it fits better.
• Comfortable with high autonomy in a fast, asynchronous, Slack-first setting. You decide within your scope and keep others informed instead of waiting for approval.
• Clear writing, since much of your impact comes through documents, definitions, and charts that people read without you in the room.
• Bonus: healthcare data, including claims (837/835/UB04), DRG and coding review, payment integrity, provider contracts, or revenue cycle.
• Bonus: data contracts, schema enforcement, ODCS-style governance, or quality gating in CI.
• Bonus: Databricks Unity Catalog, lineage, and cost or DBU accountability.
• Bonus: paginated reporting, SharePoint or Excel delivery, or other outputs that must match an exact format.
• Bonus: working alongside AI coding agents and reviewing their output against a contract you wrote.

Conditions

Remote work from anywhere in the US. The company is digital-first.

Pay is set by factors specific to each candidate, such as years and depth of experience, specific skills, and certifications. Recruiting may place you in a different level or range.

Benefits

• Greenfield work: a governed semantic layer that centralizes business logic. It is a funded priority and one of the highest-impact problems on the team.
• A strong existing base of 634 production models, 1,800+ automated tests, and solid CI guardrails, so you can focus on new problems.
• Your work carries through to the reporting layer, so you see how your decisions affect business users.
• Every project has a named business owner and defined acceptance criteria.
• Agent-driven workflows are built into daily development, and delivery velocity has grown significantly as a result.
• Work on systems that help a large healthcare payment integrity business understand and improve its performance.
• Full medical, dental, and vision coverage for employees and their families.
• Unlimited FTO.
• Competitive salary, equity, and a 401(k) with employer match.
• Salary is one part of total compensation, which also includes meaningful equity, healthcare, flexible time off, and other benefits and perks.
• A flexible, trusting environment where you can do your best work.