Data Engineering and Analytics Manager

About the role

The Division Data Engineering and Analytics Manager leads the design, delivery, and ongoing improvement of the division's data engineering and analytics foundation. This includes managing scalable data pipelines, cloud-based data platforms, and curated data assets that support reporting, analytics, and future advanced analytics use cases.

Responsibilities

• Lead the day-to-day management, support, and enhancement of the division’s data engineering environment, including Databricks and related data platform capabilities.
• Build and maintain curated, trusted, and reusable data assets that support reporting, dashboards, self-service analytics, and business intelligence.
• Lead, coach, and develop data engineering resources, including internal team members and partners as applicable.
• Drive platform data governance, partnering with business and functional stakeholders to improve data quality, lineage, controls, and ownership across the data lifecycle.
• Help build the data foundation required to support future advanced analytics, data science, and AI use cases.

Requirements

• Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Engineering, or related field.
• 5+ years of experience in data engineering, data integration, analytics engineering, or related data platform roles.
• 2+ years of experience leading teams, projects, or delivery efforts in a data and analytics environment.
• Hands-on experience with modern data platforms and cloud-based data engineering solutions, including Databricks or similar technologies.
• Strong experience in designing, building, and supporting data pipelines, ETL/ELT processes, and curated data models.
• Experience with SQL and one or more programming/scripting languages such as Python, Scala, or similar.
• Experience working with BI, reporting, or analytics teams to support trusted data consumption.
• Strong understanding of data quality, governance, controls, and metadata/lineage concepts.
• Demonstrated ability to work across business and technology teams and communicate effectively with technical and non-technical stakeholders.

Qualifications

• Experience in manufacturing, supply chain, operations, finance, or other complex enterprise environments.
• Experience with cloud data ecosystems such as Azure, AWS, or GCP.
• Familiarity with advanced analytics, machine learning, or AI data preparation requirements.
• Experience supporting enterprise-scale reporting and dashboard environments.
• Experience implementing or supporting data governance practices and operating models.
• Knowledge of DevOps, DataOps, CI/CD, and automated testing practices for data pipelines.