Data Steward

Job Summary

We are looking for an experienced Technical Data Steward with strong 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.
• Strong 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.
• Maintain business and technical metadata.
• Document and validate end-to-end data lineage.
• Identify Critical Data Elements (CDEs) and define appropriate validation rules.
• Analyze production data issues and determine whether the root cause is related to:
  • Source systems
  • Data mappings
  • Transformations
  • Master data
  • Downstream logic
• Work with Engineering teams to translate business data-quality requirements into automated technical validations.
• Perform impact analysis for schema, mapping, source-system, and business-rule changes.
• Support metadata and lineage maintenance within enterprise data catalog/governance platforms.
• Work directly with MTSC Business SMEs to understand the business meaning of data and translate business requirements into technical data rules.

Mandatory Technical Skills

SQL & Data Analysis

• Advanced SQL skills.
• Strong experience with:
  • Complex Joins
  • CTEs
  • Window Functions
  • Aggregations
  • Duplicate Detection
  • Data Validation
  • Data Reconciliation
  • Root-Cause Analysis
• Ability to independently analyze millions of records and identify patterns, anomalies, and data-quality issues.

Data Management & Stewardship

Strong hands-on experience with:

• Data Profiling
• Data Quality
• Data Validation
• Data Reconciliation
• Source-to-Target Mapping
• Data Lineage
• Metadata Management
• Master Data
• Reference Data
• Data Modeling concepts
• Large-scale Data Analysis

Preferred Technical Skills

Experience with one or more of the following is preferred:

• Python / PySpark
• Databricks
• Azure Data Platform
• Delta Lake / Lakehouse concepts
• SAP Data Analysis
• Power BI or similar analytical tools
• Alation
• Collibra
• Microsoft Purview
• Other enterprise Data Catalog / Data Governance tools
• Git / Version-controlled data artifacts
• Automated Data Quality frameworks

MedTech Supply Chain Knowledge

Strong understanding of Supply Chain data associated with:

• Material / Product
• Plant
• Storage Location
• Sales Orders
• Purchase Orders
• Inventory
• Deliveries
• Shipments
• Customers
• Suppliers / Vendors
• Manufacturing / Production Orders
• Material Movements
• Demand Planning
• Supply Planning
• ATP / Available-to-Promise
• Lead Times
• Backorders
• Order Holds / Blocks
• Fulfillment
• OTIF / Delivery Performance

Strong understanding of SAP Supply Chain data structures and relationships is highly preferred.

Data Governance & Stewardship

The candidate should have a strong understanding of:

• Data Ownership
• Business Glossary
• Critical Data Elements (CDEs)
• Metadata
• Data Classification
• Data Lineage
• Data Quality Rules
• Data Standards
• Authoritative Data Sources
• Business & Technical Definitions