Senior Data Engineer - Analytics
A Senior Data Engineer (Analytics) at Machinify turns messy healthcare operational data into trusted, governed analytics: building certified dbt/SQL dimensional models on Databricks, owning metric definitions in a semantic layer, and delivering Power BI dashboards and reports for business stakeholders. It's a modeling-and-measurement role with observability, orchestration, and heavy stakeholder co
- Compensation
- $170K–$200K / yr
- Location
- Remote - US
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Full time
- Experience
- 6+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 6+ years across analytics engineering, BI engineering, data engineering, or business analysis with production ownership of models and metrics
- Expert SQL and strong dimensional modeling instincts
- Production dbt experience: models, tests, macros, exposures, documentation, and CI
- Hands-on with a cloud lakehouse or warehouse (Databricks and Spark SQL preferred; Snowflake, BigQuery, or Redshift transfer well)
- Owned a BI semantic layer and reports on top of it, with strong visualization judgment
- Stakeholder fluency: run requirements conversations with non-technical owners and translate vague complaints into testable definitions
- Reconciliation reflex: investigate why disagreeing numbers disagree
- Git and pull-request discipline as normal practice
- Python where genuinely the right tool (API calls, file parsing, statistics, report generation)
- Comfort with high autonomy in a fast, async, Slack-first environment
- Strong writing skills for documents, definitions, and model documentation
Nice to have
- Power BI and DAX experience
- Looker/LookML, Tableau, MetricFlow, or Cube as BI tool substitutes
- Healthcare data experience: claims (837/835/UB04), DRG and coding review, payment integrity, provider contracts, or revenue cycle
- Data contracts, schema enforcement, ODCS-style governance, or quality gating in CI
- Databricks Unity Catalog, lineage, and cost/DBU accountability
- Paginated reporting, SharePoint/Excel delivery, or exact-format business reporting
- Working alongside AI coding agents and reviewing their output against a contract
Full description
Machinify is a leading healthcare intelligence company with expertise across the payment continuum, delivering unmatched value, transparency, and efficiency to health plan clients across the country. Deployed by over 85 health plans, including many of the top 20, and representing more than 270 million lives, Machinify brings together a fully configurable and content-rich, AI-powered platform along with best-in-class expertise. We’re constantly reimagining what’s possible in our industry, creating disruptively simple, powerfully clear ways to maximize financial outcomes and drive down healthcare costs.
As a Senior Data Engineer (Analytics) you own the layer where messy operational data becomes a number a business leader will act on. You’ll sit with the people who run our business, learn how the work actually happens, and turn that into certified models, metric definitions that survive scrutiny, and reports people use. Our analytics warehouse unifies four platforms into one measurement model across dozens of unique client workflows.
This is a data modeling and measurement enabling role more than a pipeline development role, though such skills will be helpful. Expect real time on calls with finance, operations, and client-facing teams, and real time in dbt, SQL, and Power BI. If you won’t ship a dashboard you can’t reconcile, you’ll fit in great.
What You’ll Do
- Run discovery with business stakeholders. Find the real need behind the request and turn it into acceptance criteria you can build against.
- Model noisy healthcare operational data into certified dimensional models across our Databricks bronze → silver → gold architecture in dbt and SQL. Our sources disagree with each other in ways that are interesting rather than trivial. Sorting that out is most of the job.
- Own metric definitions. One measure, one definition, one place, documented so a non-engineer can read it. Today a single operating metric can be re-derived in a dozen models; collapsing that into a governed semantic layer is yours.
- Build and own the visualization layer. Power BI semantic models, dashboards, and paginated reports, including refresh health and semantic-model currency. We want real visualization judgment: the right chart for the question, a hierarchy an executive can read in ten seconds, and a reason for every choice.
- Own observability for your models. Freshness and latency detection, statistical anomaly detection, and value-drift monitoring, plus the data tests, regression guards, and CI checks that keep a defect you just fixed from coming back.
- Own orchestration for what you build. dbt Cloud job design, Airflow DAGs, scheduling, dependency ordering, dependable incremental runs.