Engineering Lead Mdm

Merck Merck · Pharma · Central Bohemian, Czech Republic

Engineering Lead for Master Data Management (MDM) and Reference Data Management (RDM) solutions, focusing on designing, developing, and delivering scalable data platforms. This role involves technical leadership, coaching a team, collaborating with stakeholders, and exploring the application of AI/ML for data quality and engineering efficiency improvements.

What you'd actually do

  1. Design and develop MDM/RDM solutions using cloud tools.
  2. Deliver MDM artifacts and integrations that enable data modeling, mapping, data quality, and governance.
  3. Integrate MDM platforms with other systems (ERP, BI, metadata stores, APIs, batch/messaging) as required.
  4. Help configure and operate core MDM domains (e.g., customer, product, reference data) to meet performance and availability needs.
  5. Explore and apply AI/ML and automation where they add value (e.g., entity resolution, matching, anomaly detection, metadata inference, CI/CD automation).

Skills

Required

  • Agile Data Warehousing
  • Anomaly Detection
  • Computer Science
  • Data Engineering
  • Data Governance
  • Data Modeling
  • Data Quality
  • Data Visualization
  • Design Applications
  • Engineering Leadership
  • Machine Troubleshooting
  • Master Data Management (MDM)
  • Metadata Management
  • Python (Programming Language)
  • Reference Data Management
  • Software Configurations
  • Software Development
  • Software Development Life Cycle (SDLC)
  • Solution Architecture
  • System Designs
  • System Integration
  • Testing

Nice to have

  • APIs
  • batch processes
  • messaging
  • REST
  • Kafka
  • data integration frameworks
  • MuleSoft
  • data quality tools
  • operationalizing ML/AI features in data pipelines
  • agile
  • DevOps tools
  • JIRA
  • Confluence
  • CI/CD tooling
  • Git
  • distributed or vendor-supported teams

What the JD emphasized

  • 5+ years of hands-on experience with MDM/RDM implementations or related data integration projects.

Other signals

  • AI-driven approaches and automation to improve data quality and engineering efficiency
  • Explore and apply AI/ML and automation where they add value (e.g., entity resolution, matching, anomaly detection, metadata inference, CI/CD automation)