Manager Data Engineering

Johnson & Johnson Johnson & Johnson · Pharma · Dublin, Ireland

Manager Data Engineering role at Johnson & Johnson focused on modernizing the data landscape by building a scalable lakehouse architecture using Databricks and cloud technologies. The role involves defining data engineering strategy, architecture, governance, and execution to deliver high-quality data products, with a focus on leadership and collaboration across various teams.

What you'd actually do

  1. Lead the Data Foundation initiative to modernize the enterprise data ecosystem through a scalable lakehouse architecture and cloud-based data platform capabilities
  2. Define the data engineering strategy, target architecture, and reusable pipeline frameworks needed to deliver governed, high-quality data products
  3. Develop the strategy for a Data Supermarket that delivers business-ready data products for use across multiple functions.
  4. Translate complex business requirements and technical challenges into scalable architecture decisions and executable delivery plans
  5. Provide technical leadership for business separation activities, ensuring alignment to future-state operating models and platform continuity

Skills

Required

  • Databricks
  • PySpark
  • Spark SQL
  • Python
  • SQL
  • Azure
  • AWS
  • Modern data architecture
  • Platform design
  • Dimensional modeling
  • Data Vault 2.0
  • dbt
  • Airflow
  • Azure Data Factory
  • CI/CD pipelines
  • Automation frameworks
  • Testing practices
  • Automated testing strategies for data engineering pipelines
  • End-to-end pipeline validation
  • Data quality and integrity testing
  • Leadership
  • People management

Nice to have

  • Data Supermarket strategy
  • Data Governance
  • Data Quality
  • Observability practices
  • Monitoring
  • Lineage
  • Reliability
  • Service-level expectations

What the JD emphasized

  • modernize the enterprise data ecosystem
  • scalable lakehouse architecture
  • cloud-based data platform capabilities
  • data engineering strategy
  • target architecture
  • reusable pipeline frameworks
  • governed, high-quality data products
  • Data Supermarket
  • business-ready data products
  • scalable architecture decisions
  • executable delivery plans
  • technical leadership for business separation activities
  • future-state operating models
  • platform continuity
  • Data modeling (dimensional, Data Vault 2.0)
  • Pipeline design, modularity, and reuse
  • Engineering standards and quality controls
  • data governance
  • data quality
  • observability practices
  • monitoring
  • lineage
  • reliability
  • service-level expectations
  • automated testing strategies for data pipelines
  • validation
  • data quality and integrity testing
  • CI/CD integration
  • Databricks
  • Python
  • SQL
  • dbt
  • Airflow
  • cloud-native tools
  • vendor delivery
  • outcome-based execution
  • Product, Supply Chain business, AI/ML, Data Governance, and IT teams
  • Minimum of 8 years of experience in data engineering
  • 2 or more years in leadership or people management roles
  • Databricks, PySpark, Spark SQL
  • Python and SQL development
  • Cloud platforms (Azure preferred, AWS acceptable)
  • Modern data architecture and platform design
  • Dimensional modeling and Data Vault 2.0
  • dbt, Airflow, Azure Data Factory (or equivalent tools)
  • CI/CD pipelines, automation frameworks, and testing practices
  • designing and implementing automated testing strategies for data engineering pipelines
  • End-to-end pipeline validation
  • Data quality and integrity testing
  • Integration with CI/CD pipelines
  • leading distributed teams
  • partnering effectively with external vendors and cross-functional stakeholders