Experience:
8-10 years as a Lead Data Product Engineer in the Life Sciences / Pharma Commercial domain. Expected balance of the role: The Lead Data Product Engineer is expected to perform all three of the following areas and must demonstrate a strong balance across them: Technical leadership and hands-on engineering: design, build, review, and optimise data products and pipelines, and lead engineering delivery. Stakeholder management: act as the primary point of contact and engage confidently with business stakeholders at a KPI and business-outcome level. Governance, documentation, and transition support: provide oversight to ensure the solution is documented, governed, and supportable. 2. Technology and domain context Platform and tooling: Orchestration: Apache Airflow Cloud: AWS Data platforms: Databricks and Snowflake Source control: Bitbucket Reporting: Power BI (primary visualisation layer) Architecture: Medallion Architecture (bronze, silver, gold) is already implemented. The Lead is responsible for owning, maintaining, enhancing, and, where required, redesigning it. Business domain: Claims data and commercial performance data, including PP and NPP KPIs and their business context Field team data, and how commercial stakeholders use it for reporting 3.

Key responsibilities
Technical leadership and engineering delivery Provide technical leadership and direction across the project and lead engineering delivery in line with AZ technical standards. Hands-on design, development, and optimisation of data pipelines and data products on Airflow, Databricks, and Snowflake. Own the Medallion Architecture, ensuring the purpose and boundaries of each layer are clear and consistently applied. Review and challenge proposed architecture, data flows, integration approaches, data models, and reporting solutions. Design and manage Airflow workflows, including orchestration, scheduling, dependencies, refresh mechanisms, and operational management of pipelines. Define and apply data partitioning strategies to improve scalability and performance.



Review delivery plans and technical designs to confirm that data movement from source systems to reporting layers is feasible and appropriately controlled. Data modelling, business rules, and reporting Review proposed data models using knowledge of facts, dimensions, data grain, relationships, KPIs, and aggregation logic. Confirm that business rules and metric definitions (including PP and NPP KPIs) are clearly understood, consistently applied, and traceable to agreed requirements. Review data lineage, dependencies, ownership, and downstream consumption of Gold-layer datasets in Power BI. Assess the impact of changes to source systems, pipelines, transformation logic, Gold-layer datasets, semantic models, and reports. Issue investigation and RCA Coordinate the investigation of data discrepancies, refresh failures, performance issues, and delivery concerns. Create RCA documents for issues identified across source, transformation, and reporting layers. Ensure technical issues are assigned to the correct owners and progressed to resolution. Stakeholder management Assess whether the solution is technically and operationally ready for transition into BAU support. Maintain full knowledge of the required transition artefacts: architecture diagrams, data lineage, runbooks, monitoring, SLAs, support models, ownership, and data quality rules. Identify readiness gaps and agree remediation plans with delivery and operational stakeholders. Provide a transparent recommendation: ready for transition, ready with agreed risks, or not ready for sign-off. 4.



Required knowledge and skills Data architecture and engineering Medallion Architecture (bronze, silver, gold) and the implications of each layer Data lineage, source-to-target dependencies, ownership, and downstream consumption ETL and ELT patterns, ingestion, transformation, orchestration, scheduling, and refresh mechanisms Apache Airflow: workflow design, orchestration, and operational management Databricks: architecture, engineering practices, and hands-on implementation Snowflake: architecture, platform capabilities, and data engineering best practices Data partitioning strategies for scalability and performance optimisation Common pipeline failure points, data quality issues, integration risks, and operational dependencies End-to-end data flow from source systems through transformation layers to reporting and analytics AWS cloud services and Bitbucket-based code management Data modelling and reporting Fact and dimension tables, data grain, relationships, keys, measures, and aggregation logic KPI definitions, business rules, semantic models, and consumption of Gold-layer datasets by Power BI Investigating data discrepancies, refresh failures, and performance issues Producing RCA documentation across source, transformation, and reporting layers Domain knowledge (life sciences / pharma commercial) Claims data and commercial performance data, including PP and NPP KPIs and their business context How field teams operate and how commercial stakeholders use field data for reporting Ability to engage at a business outcome and KPI level Technical leadership and governance Experience reviewing solution documentation, delivery plans, operational models, support arrangements, and transition artefacts Understanding of risks from incomplete documentation, unclear ownership, insufficient monitoring, weak data quality controls, and unsupported dependencies Ability to make balanced recommendations based on technical evidence, business impact, delivery constraints, and operational risk Stakeholder and delivery management

📌 Lead Data Product Engineer (Chennai)
🏢 TESTQ Technologies
📍 Chennai