Job Summary

We are looking for an experienced Technical Data Steward with solid hands-on expertise in Data Analysis, Data Quality, Data Profiling, Metadata, Data Lineage, and Supply Chain Data.

The candidate will work closely with Product Managers, Business SMEs, Data Engineers, Data Architects, and Analytics teams to ensure MedTech Supply Chain data is accurate, consistent, traceable, and fit for business consumption.

Previous Johnson & Johnson (J&J;) experience and direct J&J; MedTech Supply Chain (MTSC) experience are mandatory.

Experience Required

7+ years of overall experience in Data, Analytics, Data Engineering, Data Governance, or related areas.
3+ years of experience in Data Stewardship, Data Quality, Data Analysis, Data Governance, or a closely related technical data role.
Solid hands-on SQL experience.
Experience working with large enterprise datasets and multiple source systems.
Experience with data profiling, reconciliation, data-quality validation, and root-cause analysis.
Strong understanding of data models and relationships between enterprise business entities.




Experience working directly with Data Engineering and Data Architecture teams.
Experience translating business rules into technical data-validation rules.
Strong analytical and problem-solving skills.

Key Responsibilities

Perform hands-on analysis of large and complex MedTech Supply Chain datasets.
Analyze data across multiple source systems and identify inconsistencies, gaps, anomalies, and data-quality issues.
Perform source-to-target validation and data reconciliation.
Define and implement data-quality rules covering:
Completeness
Accuracy
Consistency
Uniqueness
Timeliness
Perform data profiling and root-cause analysis for data issues.
Understand upstream source data, transformation logic, curated data, and downstream data consumption.
Define and maintain source-to-target mappings and business transformation rules.
Validate data transformations implemented by Data Engineering teams.

📌 Data Steward (Hyderabad)
🏢 Syren
📍 Hyderabad