Data Engineering Technical Lead - Hillenbrand

Position Summary:

The Data Engineering Technical Lead serves as the senior technical execution lead for the enterprise data engineering environment. This role bridges business intent, architecture, analytics, and engineering implementation to ensure scalable, reliable, and governed data solutions are successfully delivered. The individual will provide day-to-day technical leadership for data engineers, support implementation decisions, resolve technical ambiguity, guide operational support activities, and help ensure consistency across enterprise data platforms. This role combines strong hands-on technical expertise with operational leadership, problem-solving, and the ability to translate business and reporting needs into practical engineering execution. The role is heavily focused on technical coordination, engineering enablement, implementation guidance, troubleshooting, impact analysis, and operational stability within a complex enterprise data ecosystem.

Work You’ll Do:

• Serve as the primary technical lead and escalation point for enterprise data engineering initiatives
• Bridge business requirements, architectural standards, and engineering implementation
• Partner with business analysts, architects, BI teams, DevOps, and data engineers to support successful solution delivery
• Interpret and clarify technical implementation requirements for data engineering teams
• Guide implementation decisions across Databricks pipelines, transformations, and data models
• Review engineering implementations for consistency, scalability, maintainability, and alignment to standards
• Support troubleshooting and root cause analysis for data quality issues, failed pipelines, performance concerns, and production defects
• Act as L1/L2 support lead for enterprise data platform operational issues
• Perform lineage and downstream impact analysis for data model and pipeline changes
• Guide implementation of reusable engineering patterns, medallion architecture, and gold-layer datasets
• Coordinate defect triage, release support, deployment validation, and production stabilization activities
• Support adoption of engineering standards, CI/CD processes, governance controls, and operational best practices
• Mentor and guide data engineers on technical implementation approaches and enterprise standards
• Drive consistency across engineering teams, platforms, and data products
• Document technical patterns, implementation standards, operational procedures, and support processes

Technical Environment

· Databricks

· Spark / PySpark

· Delta Lake

· SQL

· Python

· Power BI

· Azure Data Factory