Data Warehouse Engineer

Data Warehouse / Modeling Engineer Level: Mid–Senior Experience: minimum 6 years ( 6 to 10 years) Location: Magarpatta, Pune - Hybrid (2-3 days in office) *** Due to high volume of applicants, only relevant profiles will receive a response - hence please ensure you apply as per the years of exp and skill sets. *** About Simplify Healthcare: Simplify Healthcare , a Simplify Group company , is a leading healthcare technology company focused on transforming how U.S. health plans (Payers) operate across benefits, provider, and claims functions. We build cloud-based, AI-driven SaaS platforms that modernize core payer workflows and enable scalable, compliant, and efficient operations. Our flagship platform, Simplify Health Cloud™ , brings together enterprise-grade solutions for product configuration, benefits administration, provider lifecycle management, claims operations, and experience orchestration.

Designed specifically for the U.S. health insurance ecosystem, our platforms are built to support real-world regulatory complexity, operational scale, and continuous change. Headquartered in Chicago , with a Global Development & Delivery Center in Pune and a global workforce of 900+ professionals, Simplify Healthcare supports more than 70 U.S. health plans. We are a bootstrapped, debt-free organization with over 18 years of profitable growth, combining long-term stability with a strong execution culture.

Our work has been consistently recognized by Deloitte (Technology Fast 500™), Inc. (Inc. 5000), FORTUNE, IDC, and Gartner. As we enter our next phase of growth, we continue to invest in applied AI, platform modernization, and leadership talent to shape the future of payer technology. ** Data Engineer experience remains a must-have along with practical, hands-on AI/ML experience.** Role Summary We are looking for a Data Warehouse / Modeling Engineer with deep expertise in dimensional modeling, modern warehouse platforms, and dbt. This role is central to building well-modeled, well-tested, and well-documented analytical data products that power business intelligence and analytics.

Key Responsibilities Design and implement dimensional models using Kimball, Data Vault 2.0, and Star schema approaches. Build and maintain dbt projects (core or cloud) with strong testing, documentation, and data contracts. Implement Slowly Changing Dimensions (SCD types 1, 2, and 3) and historical tracking patterns.

Develop and optimize complex SQL on Snowflake, BigQuery, or Redshift. Manage data catalog and lineage tooling to support discoverability and governance. Tune query and warehouse performance for cost-effective, scalable analytics.

Required & Preferred Skills Must Have AWS / Azure / GCP (at least 2 clouds) Data modeling (Kimball, Data Vault 2.0, Star schema) dbt (core or cloud) Expert-level SQL NoSQL — GraphDB / DocumentDB / Key-Value Warehouse platforms (Snowflake, BigQuery, or Redshift) Slowly Changing Dimensions (SCD types 1, 2, 3) Data catalog & lineage tools Query performance tuning & optimization Testing, documentation & data contracts Good to Have Python (pandas, NumPy, scikit-learn) LLM / GenAI pipeline orchestration Vector databases (Pinecone, Weaviate, pgvector) Advanced SQL Feature stores (Feast, Tecton) ML pipeline tools (MLflow, Kubeflow, SageMaker)