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)