Analytics Engineering Lead

Responsibilities:

• Partner with IT, enterprise architecture, and platform enablement teams to scale the Global Operations enterprise data lakehouse and reporting ecosystem.
• Own the Global Operations analytical data architecture, including silver and gold-layer data products, dimensional models, semantic assets, and integration patterns aligned with enterprise standards.
• Design and build cleansed, conformed, and consumption-ready data products that integrate data across enterprise systems and support executive reporting, analytics, and decision intelligence.
• Develop and govern certified semantic models, standardized KPI frameworks, and reusable business logic that establish a single source of truth for performance measurement across Global Operations.
• Implement data governance practices including data quality controls, reconciliation, metadata, lineage, certification, and ownership standards to ensure trusted reporting assets.
• Automate and optimize data transformation workflows using SQL, Python, PySpark, and cloud-native data engineering services.
• Establish DataOps and platform management practices including source control, automated testing, CI/CD, monitoring, performance optimization, security controls, and production support.
• Design AI-ready data products and knowledge assets that support advanced analytics, machine learning, generative AI, and decision intelligence capabilities.

Qualifications

• Bachelor's degree in Data Engineering, Data Science, Computer Science, Statistics, or a related field, or equivalent professional experience; Master's degree preferred.
• 8+ years of experience in analytics engineering, data modeling, data warehousing, or data platform development, with deep expertise in dimensional modeling and modern data architectures.
• Expert-level SQL and strong proficiency in Python/PySpark, with experience building scalable data pipelines, lakehouse architectures, and governed data products.
• Highly experienced with modern cloud data platforms such as Microsoft Fabric, Databricks, Snowflake, or equivalent technologies, including orchestration and transformation frameworks.
• Demonstrated experience architecting enterprise data products, governance frameworks, and reusable analytical assets, including KPI frameworks, metadata, and data quality controls, to support scalable analytics, machine learning, AI solutions.
• Strong understanding of ERP and operational business processes, including SAP ECC data structures and modules such as MM, PP, QM, and SD; familiarity with Oracle Agile PLM is preferred.
• Ability to influence technical and business stakeholders and communicate architectural concepts across a matrixed organization.
• Experience leveraging DevOps and collaboration platforms, including GitHub, Confluence, and Jira, to support agile delivery, version control, documentation, and product lifecycle management.

Preferred Qualifications:

• Previous experience supporting global supply chain, manufacturing, quality, or regulatory operations in a complex enterprise environment.
• Experience supporting ERP modernization initiatives, including legacy ERP environments, system consolidations, data migrations, and transformation of enterprise data assets to modern analytics platforms.
• Experience driving adoption of enterprise data products, governance practices, and self-service analytics capabilities.
• Relevant Microsoft Fabric, cloud or data engineering certifications such as MS DP-600 or MS DP-700.