Manager, Data Management

At Johnson & Johnson,we believe health is everything.

Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented, treated, and cured,where treatments are smarter and less invasive, andsolutions are personal.Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity.Learn more at jnj.com .

As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world.

We provide an inclusive work environment where each person is considered as an individual.

At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

Job Function:
Data Analytics & Computational Sciences

Job Sub Function:
Data Engineering

Job Category:
Scientific/Technology

All Job Posting Locations:
Bangalore, Karnataka, India

Job Description:

The Manager, Data Management is responsible for leading the delivery of trusted, governed, and reusable data products that enable reporting, analytics, and AI use cases.

This role partners with business, analytics, and technology teams to translate priorities into a scalable data roadmap across ingestion, transformation, modeling, and data quality.

The ideal candidate blends people leadership with hands-on data engineering and data governance expertise to ensure reliable pipelines, strong stewardship, and secure access to curated datasets and semantic-ready assets.

Key Responsibilities

• Strategic leadership: Execute the data management roadmap (data products, pipelines, quality, AI readiness, and governance) aligned to business priorities and analytics/BI needs.

• Data product ownership: Own the lifecycle of curated datasets and data products (e.g., Spend/Contract/Supplier/Category data domains as applicable), including documentation, refresh cadence, and change control.

• Data engineering: Lead design and delivery of scalable ingestion and transformation pipelines across enterprise platforms (e.g., Databricks, Data Fabric), ensuring reliability, performance, and cost awareness.

• Data quality: Establish data quality controls, validation rules, and monitoring/alerting to improve accuracy, completeness, timeliness, and trust in downstream reporting and AI use cases.

• Data governance & stewardship: Partner with Data Governance to implement metadata, lineage, glossary definitions, and access standards; ensure clear ownership and stewardship across key data elements.

• Cross-functional collaboration: Collaborate with BI and business process owners to translate requirements into governed data models and consumable assets (semantic-ready tables, views, and features).

• Accountability: Define and manage SLAs for refresh timeliness, incident response, and data quality; drive root-cause analysis and continuous improvement.

Qu...