Senior Data Engineer - Analytics at Machinify

Job 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. Write it down. Design documents, model documentation, a CI-enforced knowledge base, and business-readable handoff packets, so whoever inherits your work doesn’t have to re-derive it. Automate the recurring work. We codify repeating work into repeatable, testable, AI-assisted workflows instead of checklists in someone’s notes. You should be comfortable directing LLMs and supervising what they produce. What You Bring 6+ years across analytics engineering, BI engineering, data engineering, or business analysis, with production ownership of the models and metrics you built. Expert SQL and strong dimensional modeling instincts. You spot a grain mismatch in someone else’s model before you run it. Production dbt: models, tests, macros, exposures, documentation, and CI. You have opinions about project structure and can defend them. Hands-on with a cloud lakehouse or warehouse. Databricks and Spark SQL preferred; Snowflake, BigQuery, or Redshift transfer well. Real visualization talent. You’ve owned a BI semantic layer and the reports on top of it, not just the tables underneath. Power BI and DAX strongly preferred; Looker/LookML, Tableau, MetricFlow, or Cube are credible substitutes. Bring dashboards you designed and be ready to say what you left out. Stakeholder fluency. You can run a requirements conversation with a non-technical owner, turn a vague complaint into a testable definition, explain a data problem as business risk, and tell someone their metric is wrong without losing them. A reconciliation reflex. When two numbers disagree you don’t average them, escalate them, or trust the newer one. You find out why. Git and pull-request discipline as normal practice rather than overhead. Python where it’s genuinely the right tool (API calls, file parsing, statistics, report generation), and the judgment to reach for SQL where it isn’t. Comfort with high autonomy in a fast, async, Slack-first environment. You decide inside your lane and inform, rather than queuing decisions for permission. Strong writing. Much of your impact is a document, a definition, or a chart someone reads without you in the room. Bonus points for Healthcare data: claims (837/835/UB04), DRG and coding review, payment integrity, provider contracts, or revenue cycle. Data contracts, schema enforcement, or ODCS-style governance; quality gating in CI. Databricks Unity Catalog, lineage, and cost/DBU accountability. Paginated reporting, SharePoint/Excel delivery, or other “the business needs it in exactly this format” surfaces. Working alongside AI coding agents and reviewing what they produce against a contract you wrote. Why Join Us Own a real greenfield problem. We’re building a governed semantic layer that centralizes business logic. It’s a funded priority and one of the highest-impact problems on the team. Strong engineering foundation. We already have 634 production models, 1,800+ automated tests, and strong CI guardrails. You can focus on solving new problems instead of building basic engineering discipline from scratch. See your work reach the business. This team owns the work all the way to the reporting layer, so you’ll see how your decisions affect real business users. Work directly with stakeholders. Every project has a clear business owner and defined acceptance criteria, so you know who you’re building for and what success looks like. Use AI in the actual engineering workflow. We’ve built agent-driven workflows into our day-to-day development process, and our delivery velocity has grown significantly as a result. If you’re interested in actually using AI to change how software gets built, you’ll have plenty of opportunity here. Make an impact in healthcare. The systems you build help a large-scale healthcare payment integrity business understand and improve its performance. What We Offer Work from anywhere in the US! Machinify is digital-first. Full Medical/Dental/Vision for employees & their families Flexible and trusting environment where you’ll feel empowered to do your best work Unlimited FTO Competitive salary, equity, 401(k) including employer match The salary for this position is based on an array of factors unique to each candidate: Such as years and depth of experience, set skills, certifications, etc. We are hiring for different levels, and our Recruiting team will let you know if you qualify for a different role/range. Salary is one component of the total compensation package, which includes meaningful equity, excellent healthcare, flexible time off, and other benefits and perks. Pay range: $170,000 - $200,000 Equal Employment Opportunity at Machinify We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, or veteran status. We are proud to be an equal opportunity workplace. Machinify is an employment at will employer. We participate in E-Verify as required by applicable law. In accordance with applicable state laws, we do not inquire about salary history during the recruitment process. If you require a reasonable accommodation to complete any part of the application or recruitment process, please let our recruiters know. See our Candidate Privacy Notice at: machinify.com

Requirements

Must-haves

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.
Own orchestration for what you build. dbt Cloud job design, Airflow DAGs, scheduling, dependency ordering, dependable incremental runs.
Write it down. Design documents, model documentation, a CI-enforced knowledge base, and business-readable handoff packets, so whoever inherits your work doesn’t have to re-derive it.
Automate the recurring work. We codify repeating work into repeatable, testable, AI-assisted workflows instead of checklists in someone’s notes. You should be comfortable directing LLMs and supervising what they produce.
6+ years across analytics engineering, BI engineering, data engineering, or business analysis, with production ownership of the models and metrics you built.
Expert SQL and strong dimensional modeling instincts. You spot a grain mismatch in someone else’s model before you run it.
Production dbt: models, tests, macros, exposures, documentation, and CI. You have opinions about project structure and can defend them.
A reconciliation reflex. When two numbers disagree you don’t average them, escalate them, or trust the newer one. You find out why.
Git and pull-request discipline as normal practice rather than overhead.
Python where it’s genuinely the right tool (API calls, file parsing, statistics, report generation), and the judgment to reach for SQL where it isn’t.
Comfort with high autonomy in a fast, async, Slack-first environment. You decide inside your lane and inform, rather than queuing decisions for permission.
Strong writing. Much of your impact is a document, a definition, or a chart someone reads without you in the room.
Healthcare data: claims (837/835/UB04), DRG and coding review, payment integrity, provider contracts, or revenue cycle.
Data contracts, schema enforcement, or ODCS-style governance; quality gating in CI.
Databricks Unity Catalog, lineage, and cost/DBU accountability.
Paginated reporting, SharePoint/Excel delivery, or other “the business needs it in exactly this format” surfaces.
Working alongside AI coding agents and reviewing what they produce against a contract you wrote.

Nice-to-haves

Hands-on with a cloud lakehouse or warehouse. Databricks and Spark SQL preferred; Snowflake, BigQuery, or Redshift transfer well.
Bonus points for

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aisqlpythonapi

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)