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.

- 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

- 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 transparent 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 clear recommendation: ready for transition, ready with agreed risks, or not ready for sign-off.

- 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