About this role
The Opportunity
Join our Acceleration Center India and help shape the future of business for our diverse client portfolio across geographies and jurisdictions. You’ll work at the heart of global teams across Advisory, Assurance, Tax and Business Services—solving real client challenges through connected collaboration. We’ll help you grow your skills so you can go further. With hands-on learning, cutting-edge tools and an inclusive culture, this is your opportunity to do inspiring work that makes a difference—every day.
As a Data Modeler – Senior Associate, you will play a pivotal role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Within our Data Science practice, you will focus on leveraging advanced analytics and statistical techniques to extract insights from large datasets and drive data-driven decision-making. As a Senior Associate, you will build meaningful client connections and learn how to manage and inspire others. You will navigate increasingly complex situations, growing your personal brand and deepening your technical skills. You are expected to anticipate the needs of your teams and clients, delivering quality solutions even when the path forward is unclear.
In this role at PwC Acceleration Center India, you will engage in exploratory and descriptive analysis, statistical modeling, and creating data visualizations to solve complex business problems and inform strategic decisions. You will use a broad range of tools and methodologies to generate new ideas and solve problems, while upholding professional and technical standards. This position offers a unique opportunity to develop a deeper understanding of the business context and how it is evolving, allowing you to grow both personally and professionally.
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 strong 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.