Data Engineer

Position Summary:

Our partner is a global manufacturer modernizing its ERP-driven data environment into a cloud-native platform for trusted analytics and forecasting. They are growing their team to support the build-out and ownership of the data foundation that enterprise analytics and forecasting depend on. The environment is Databricks and Azure, with ERP data at the core, and the challenge is turning complex, scattered systems into trusted, high-performance data models the business can rely on. The focus is on data modeling, pipeline reliability, performance tuning, and governance, not dashboards or surface-level reporting. Data accuracy, performance, and reliability matter because leaders depend on these systems to make operational and financial decisions.

Experience and Education:

• BS in Computer Science, Information Systems, Software Engineer or equivalent technical field
• Experience building enterprise data platforms using Databricks and Azure
• Background in designing and owning data models sourced from ERP systems
• Experience working with SAP or other large-scale ERP environments
• History of owning data pipelines end-to-end in production environments
• Exposure to forecasting, analytics, or ML-enabled data use cases
• Experience working with globally distributed or offshore teams

Skills and Strengths:

• Databricks
• Azure
• Data engineering
• Data modeling
• ERP data environments
• SAP
• SQL
• Python
• Lakehouse architecture
• ETL design
• Data orchestration
• Data quality frameworks
• Performance optimization
• Enterprise data governance
• Analytics enablement
• Forecasting data pipelines
• Machine learning support
• Systems tradeoff analysis
• Cross-team collaboration
• Technical documentation

Primary Job Responsibilities:

• Own the Databricks-based data engineering layer used across the enterprise
• Design and maintain high-quality data models sourced from ERP systems
• Ensure data pipelines are reliable, scalable, and performant
• Standardize ETL patterns, orchestration, and data quality checks
• Improve lakehouse performance and long-term maintainability
• Partner closely with BI leadership to enable trusted analytics and forecasting
• Support ML and advanced analytics use cases through strong data foundations
• Establish and reinforce enterprise data governance practices
• Collaborate with offshore and onshore teams to ensure consistency
• Decide when work requires immediate fixes versus backlog prioritization
• Clearly explain engineering tradeoffs across cost, performance, and maintainability
• Reduce operational firefighting by improving platform stability
• Document data models, pipelines, and standards for reuse
• Act as a senior technical voice without direct people management
• Continuously improve the platform based on business and technical needs