Senior Analytics Engineer — Enterprise Data Warehouse
Machinify India Bangalore, Karnataka, India
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About the role
You will own the enterprise data modeling layer, transforming operational data into certified metrics and reports for business leaders. This involves building dimensional models, managing semantic layers, and ensuring data quality through automated testing and orchestration.
What they look for
Requirements
The role requires 6+ years of experience in analytics or data engineering with expert-level SQL and dimensional modeling skills. Proficiency in dbt, cloud lakehouse platforms like Databricks, and BI tools such as Power BI is essential.
Full description
Senior Analytics Engineer — Enterprise Data Warehouse
Location: Bengaluru, India
About Machinify
Machinify builds the AI operating system for healthcare payment intelligence, combining the minds of industry experts with the power of technology. US healthcare wastes billions of dollars a year inside the claims payment workflow, and Machinify has built a platform to fix that, backed by more than 40 years of payment integrity expertise and a product suite spanning the full claims lifecycle from pre-pay through post-pay. Today, 85+ customers rely on us, including 18 of the top 20 US health plans, covering more than 270 million member lives across commercial, Medicare, and Medicaid. We process over 3 petabytes of claims data and return more than $6 billion in savings and recoveries to our customers every year, with 99% customer retention.
We're now opening a new hub in Bengaluru that isn't a satellite office or a support function: it's a genuine extension of one global Machinify, where local leaders run real teams and own real product and outcomes. The problems our teams solve are hard by design, which is exactly why they haven't been solved before, and why the work is worth doing. Joining now means building Machinify's India presence- its people, culture, and way of working- from the ground up, with real influence over what gets built and how.
About the Role
This role sits within Enterprise Data & Analytics.
You'll own the layer where messy operational data becomes a number a business leader will act on. You'll work closely with business operators, learn their real workflows, and turn them into certified models, durable metric definitions, and reports people actually use. The analytics warehouse combines four platforms into a single measurement model spanning dozens of distinct client workflows. It runs 634 production models with 1,800+ automated tests and strong CI guardrails. The role centers on data modeling and measurement more than pipeline building. Expect regular calls with finance, operations, and client-facing teams, plus hands-on work in dbt, SQL, and Power BI. A governed semantic layer that centralizes business logic is a funded, high-impact greenfield priority. AI agents are already part of daily development.
What You Will Own
- Stakeholder discovery: uncover the true need behind a request and turn it into acceptance criteria to build against.
- Healthcare data modeling: build certified dimensional models from noisy operational data across a Databricks bronze-silver-gold architecture using dbt and SQL. Resolving conflicting sources is most of the job.
- Metric definitions: one measure, one definition, one place, documented so non-engineers can follow it. Help consolidate metrics into a governed semantic layer.
- Visualization: build and own Power BI semantic models, dashboards, and paginated reports, including refresh health.
- Observability: own freshness, anomaly, and drift monitoring, plus data tests, regression guards, and CI checks so fixed defects stay fixed.
- Orchestration: dbt Cloud job design, Airflow DAGs, scheduling, and reliable incremental runs.
- Documentation and automation: write design docs, model docs, and business-readable handoffs. Turn recurring work into repeatable, AI-assisted workflows.
Qualifications
Required
- 6+ years in analytics engineering, BI engineering, data engineering, or business analysis, with production ownership of the models and metrics they built.
- Expert SQL and strong dimensional modeling instincts (e.g., catching a grain mismatch before running a model).
- Production dbt experience: models, tests, macros, docs, and CI.
- Hands-on with a cloud lakehouse or warehouse. Databricks/Spark SQL preferred; Snowflake, BigQuery, or Redshift transfer well.
- Real visualization skill: has owned a BI semantic layer and the reports on it, not just the tables. Power BI/DAX strongly preferred; Looker, Tableau, MetricFlow, or Cube acceptable.
- Stakeholder fluency: can run requirements conversations with non-technical owners and turn vague complaints into testable definitions.
- A reconciliation habit: when numbers conflict, investigates the cause instead of averaging, escalating, or trusting the newer one.
- Git and pull-request discipline. Python where it fits.
- Strong writing. Comfortable with high autonomy in an async, Slack-first setting with US teams.
Preferred
- Healthcare data: claims (837/835/UB-04), DRG and coding review, payment integrity, provider contracts, revenue cycle.
- Data contracts, schema enforcement, CI quality gates.
- Databricks Unity Catalog, lineage, cost/DBU accountability.
- Paginated reporting, SharePoint/Excel delivery.
- Experience directing AI coding agents and reviewing their output.
Tech Stack
Databricks (medallion), Spark SQL, Unity Catalog, dbt / dbt Cloud, Power BI (DAX, semantic models, paginated reports), Airflow, Python, Git/CI, Slack.
Equal Employment Opportunity at Machinify
Machinify is committed to providing equal employment opportunities and fostering an inclusive workplace. Employment decisions are based on business needs, job requirements, and individual qualifications, without discrimination on any status protected under applicable law. If you require a reasonable accommodation to complete any part of the application or recruitment process, please let our recruiting team know.
