Engineering Manager, Enterprise Data Warehouse & Analytics

Machinify; United States; Remote; USD 150,000 - 210,000; Full time

Benefits offered by this job

Remote work in the US

Medical/Dental/Vision

Equity

401(k) employer match

Unlimited PTO

Job summary

Machinify is seeking an Engineering Manager to lead a data warehouse and analytics team focused on a Databricks lakehouse. You will own the dimensional model, design ETL/ELT pipelines, and govern metrics used across Power BI dashboards and LLM queries.

You will collaborate with Product and Operations to shape requirements, deliver reliable metrics, and modernize orchestration. A hands-on manager with strong SQL/dbt and cloud data engineering experience is essential.

Qualifications

• Over 10 years in data warehousing, data engineering, or analytics engineering.
• At least 3+ years as a hands‑on engineering manager.
• Expertise in dimensional modeling: grain, conformed dimensions, SCDs, bus matrix.
• Expert SQL and dbt: complex SQL, tests, macros, CI.
• Databricks in production: Spark SQL, Delta Lake, Unity Catalog."
• Knowledge of BI semantic layers and governance for metrics (Power BI, dbt Semantic Layer/MetricFlow).
• Experience with ETL/ELT orchestration (Airflow) and Python for pipelines.
• Data quality, tests, reconciliation, observability, and CI/CD for data.

Responsibilities

• Own the data model: design and evolve the conformed dimensional model and standards; review new models for grain and conformance.
• Build and run pipelines: design ETL/ELT from sources through bronze → silver → gold on Databricks with incremental loads and data contracts.
• Own the semantic layer: build governed metrics and semantic models for dashboards and LLM queries.
• Deliver reliable operational metrics: turn KPI definitions into certified models and metrics; reconcile to source and deliver via dashboards.
• Assure the numbers: reconciliation, data tests, freshness, anomaly monitoring, SLOs, CI/CD for data.
• Modernize orchestration: move dbt orchestration from dbt Cloud to Airflow with no data freshness gaps.
• Partner with Product: translate requirements into architectural plans, sequencing, estimates, and trade-offs.
• Lead the team: coach and hire analytics engineers/BI analysts; enforce code review, testing, docs, and AI-assisted development.

Skills

Leadership

Cross-functional collaboration

Mentoring

Communication

Tools

Databricks

dbt

Airflow

Power BI

SQL

Python

Delta Lake

Unity Catalog

Snowflake/BigQuery

Job description

Engineering Manager, Enterprise Data Warehouse & Analytics

Remote - US

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.

Machinify brings together several payment integrity and recovery businesses for health plans. Each one produces numbers that executives, operations leaders, and clients act on. Your job is to make sure there is one warehouse, one conformed model, and one definition of every number.

The foundation is already in production: a Databricks lakehouse with 600+ dbt models, 1,800+ automated tests, Power BI semantic models, and LLM self-service on governed metrics. Your first priority is operational measurement : building the trusted metrics our Operations leaders run their business on. Next, you'll extend the warehouse to new business domains with a repeatable, template-driven approach.

This is a hands‑on Engineering Manager role on a flat team. You'll manage engineers and analysts directly and spend about half your time building: designing the dimensional model, writing and reviewing code, and setting the engineering standards. You'll partner closely with a Product counterpart who owns priorities and stakeholder intake, so you can focus on architecture, delivery, and the team.

What You’ll Do

• Own the data model. Design and evolve the conformed dimensional model and the standards the team builds against. Review every new model for grain and conformance.
• Build and run the pipelines. Design ETL/ELT from source systems through bronze → silver → gold on Databricks, with incremental loads, data contracts, and Airflow orchestration.
• Own the semantic layer. Build governed metrics in Unity Catalog metric views and Power BI semantic models, so every dashboard, report, and LLM query uses the same definitions.
• Deliver reliable operational metrics. Turn KPI definitions from Product and Operations into certified models and metrics, reconciled to source and delivered through dashboards and self-service.
• Assure the numbers. Own reconciliation, data tests, freshness and anomaly monitoring, SLOs, and CI/CD for data.
• Modernize orchestration. Lead the move of dbt orchestration from dbt Cloud to Airflow without gaps in data freshness.
• Partner with Product. Work with your Product counterpart and business stakeholders to shape requirements. You own the technical plan: architecture, sequencing, estimates, and trade-offs.
• Lead the team. Manage, coach, and hire analytics engineers and BI analysts. Run sprint execution and set the bar for code review, testing, documentation, and AI-assisted development.

What You Bring

• 10+ years in data warehousing, data engineering, or analytics engineering, including 3+ years as a hands‑on engineering manager .
• Expert in dimensional modeling: grain, fact types, conformed dimensions, slowly changing dimensions, bridge tables, and the bus matrix. Has built an enterprise warehouse across multiple source systems and owned it in production
• Expert SQL and dbt: complex SQL and performance tuning; dbt models, tests, macros, packages, and CI
• Databricks in production: Spark SQL, Delta Lake, Unity Catalog, and medallion architecture. Deep Snowflake or BigQuery experience will be considered
• Semantic layer: has built governed metrics (Databricks metric views, dbt Semantic Layer/MetricFlow, Cube, or Power BI semantic models) and the BI on top. Power BI/DAX preferred
• ETL/ELT and orchestration: has designed and run production pipelines with incremental loads, snapshots or CDC, and Airflow (or a similar orchestrator). Python for pipeline work
• Data quality: testing, reconciliation, observability, and CI/CD for data
• Cross-functional partner: works well with a Product counterpart and business stakeholders. Turns requirements into technical designs and explains trade-offs in plain language

Nice to have

• Healthcare claims or payment integrity data
• Building operational KPIs with business operators
• LLM-based self-service analytics (e.g., Databricks Genie)
• Experience leading teams across time zones

What We Offer

• Workfrom anywhere in the US!Machinifyis digital-first.
• Full Medical/Dental/Vision for employees & their families
• Flexible and trusting environment whereyou’llfeel 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.

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/