Pay
In line with similar jobs
$170K–$200K against $122K–$190K, the middle half of what similar US postings state.
From 321 postings in the last year. Stated ranges, not salaries paid.
Reads like an Analytics Engineer role: dbt models and clean tables.
Remote - US
Live, first seen by us 41 days agoThe 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 MachinifyRead the full posting
Listed location: Remote - USRead the full posting
Employment type in the employer’s posting: Full-timeFrom the employer’s posting
Run discovery with business stakeholders and turn requests into testable acceptance criteria
Model operational data into certified dimensional models in dbt and SQL on Databricks
Own metric definitions and build a governed semantic layer
Build and own Power BI semantic models, dashboards, and paginated reports
Own observability, data tests, and CI checks for your models
Run discovery with business stakeholders.
Model noisy healthcare operational data into certified dimensional models across our Databricks bronze → silver → gold architecture in dbt and SQL.
Own metric definitions. One measure, one definition, one place, documented so a non-engineer can read it.
Build and own the visualization layer. Power BI semantic models, dashboards, and paginated reports
Own observability for your models. Freshness and latency detection, statistical anomaly detection, and value-drift monitoring
6+ years across analytics engineering, BI engineering, data engineering, or business analysis, with production ownership of the models and metrics you built.
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.
From the employer’s posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
In line with similar jobs
$170K–$200K against $122K–$190K, the middle half of what similar US postings state.
From 321 postings in the last year. Stated ranges, not salaries paid.
Day 41
Similar jobs usually come down between day 6 and day 47.
From 1,409 similar postings that came down. Coming down is not always a hire.
11 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- 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.
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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
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We file this job under Analytics Engineer, by the work the posting describes.
Analytics engineers turn raw warehouse data into clean, tested and documented tables that analysts and dashboards can rely on, often with dbt.
Expect strong SQL, data modeling, Git, and a warehouse such as Snowflake, BigQuery or Redshift.
A data engineer gets data into the warehouse; an analytics engineer shapes it once it is there and works closer to the analysts who use it.
The government has no code for analytics engineers, so these figures are for the closest group, database architects, the same group we use for data engineers.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
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
What You Bring
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What We Offer
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
Selected passages from the saved posting. Check the full description for conditions and exceptions.
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
Remote - US
Working pattern and location restrictions need checking in the full posting.
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: https://www.machinify.com/candidate-privacy-notice/