Manager, Data Management

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions 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.

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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.

 

Qualifications

·        Master's degree in Computer Science, Information Systems, Data Engineering, Analytics, or a related field (or equivalent experience).

·        8+ years of experience in data engineering, data management, or data platform roles, including delivery of production data pipelines.

·        Strong SQL skills and experience with data modeling, ETL/ELT patterns, and orchestration; proficiency with modern data platforms (e.g., Databricks, Data Fabric) and cloud storage/compute concepts.

·        Experience implementing data quality frameworks, validation controls, metadata/lineage practices, and governed access patterns.

·        Experience supporting production data products (monitoring, incident management, and performance tuning) with defined SLAs.

·        Ability to translate business requirements into durable data products and to partner effectively with BI/reporting teams and process owners.

·        Strong written and verbal communication skills, including the ability to explain data concepts, tradeoffs, and risks to technical and non-technical audiences.

·        Experience applying AI-assisted technologies and methodologies to accelerate data management and stewardship with established review/approval steps to validate outputs and mitigate risk (privacy, bias, and hallucinations).

 

Preferred Skills

·        Experience building data products that support semantic layers and enterprise self-service analytics.

·        Familiarity with data cataloging, lineage, and observability tooling.

·        Experience with CI/CD for data pipelines (version control, automated testing, release automation).

·        Knowledge of procurement data domains and processes (S2C and/or P2P), including common master data objects and KPIs.

·        Experience partnering with Security/Privacy to implement controls such as row-level security, masking, and audit logging.

 

 

Required Skills:

 

 

Preferred Skills:

Advanced Analytics, Agility Jumps, Coaching, Critical Thinking, Data Engineering, Data Governance, Data Modeling, Data Privacy Standards, Data Science, Digital Fluency, Execution Focus, Hybrid Clouds, Organizing, Presentation Design, Technical Development, Technical Writing, Technologically Savvy