Responsibilities

- Design, develop, and maintain Conceptual, Logical, and Physical data models for healthcare data warehouses, data marts, and lakehouses.

- Produce each modelling layer as a reviewable, standalone artifact - a conceptual model with clean definitions, a platform-agnostic logical model with explicit grain, and a physical model committed to concrete types, clustering, and materialization.
- Build and maintain complex data diagrams and enterprise entity-relationship maps that stakeholders can actually read and trust.
- Spec and design end-to-end data products from the dashboard / self-serve semantic layer at the top, down through business-object mapping, into data models and the physical store.
- Design semantic / metrics layers that plug cleanly into BI tools (Power BI, Tableau) so analysts and business users can self-serve without re-deriving logic.
- Translate reporting and KPI requirements into durable model structures rather than one-off pulls.
- Forward / Reverse engineering: generate physical SQL- DDL from logical models, and reverse-engineer existing (often legacy) healthcare and commercial databases into clean visual schemas.
- Query optimization: Advanced ANSI SQL and analytical queries to parse hierarchical and longitudinal healthcare data (patient journeys, claims histories, HCP/HCO hierarchies).
- Performance tuning: Indexing strategies, partitioning, clustering keys, distribution styles, and execution-plan analysis to keep queries fast on very large patient / claims / sales datasets.
- Lakehouse paradigm: Model for decoupled storage and compute with practical Delta Lake / lakehouse experience (medallion architecture, incremental models).
- Author and maintain Source-to-Target Mapping (STTM) documents, data definitions, and metadata dictionaries.
- Define and enforce data lineage, data-quality KPIs, and reconciliation logic so the model is auditable end-to-end.




- Create data structures for US pharma domains — Electronic Health Records (EHR/EMR), clinical performance metrics, and/or pharma commercial data (sales, patient, market access, claims, patients).
- Model the messy realities of the domain: HCP/HCO master data, longitudinal patient/claims data, specialty pharmacy feeds, and affiliation hierarchies.
- Bake privacy-by-design into the models to support HIPAA compliance and general data-security guidelines (PHI/PII handling, masking, role-based access, de-identification).
- Work shoulder-to-shoulder with data engineers, clinical/commercial stakeholders, and BI analysts to optimize pipelines and make sure reporting requirements are genuinely met — not just technically satisfied.

What You Must Have

- At least a Bachelor's degree
- At least 4 years of experience
- Oral and written proficiency in English required

What Sets You Apart

- Proven experience across conceptual logical physical modelling for warehouses, marts, and lakehouses (star / snowflake schemas, dimensional modelling; Data Vault 2.0 a robust plus).
- Hands-on with a modelling tool: erwin Data Modeler, ER/Studio, SqlDBM, or PowerDesigner — including forward-engineering DDL and reverse-engineering existing schemas.
- Expert ANSI SQL with real performance-tuning depth (execution plans, partitioning, clustering).
- Production experience on at least one modern cloud data platform: Snowflake and/or Databricks / Delta Lake (Redshift, BigQuery, Synapse also relevant).
- Comfort with the transformation layer — dbt for physical modelling / ELT and exposure to orchestration (Airflow, ADF).
- Demonstrated ownership of STTMs, data dictionaries, metadata, lineage and profiling as first-class deliverables.
- Experience with healthcare and/or pharma / life-sciences data. Strong signal: familiarity with IQVIA / IMS, Symphony/claims data, Veeva, EHR/EMR, RWD/RWE, Digital Marketing Channels and MDM for HCP/HCO.
- Working understanding of HIPAA and PHI/PII handling.

📌 Data Modeller (Hyderabad)
🏢 PwC
📍 Hyderabad