Data Engineer Lead

Dorman Products is seeking a Data Engineering Lead to build and scale modern data engineering capabilities while supporting our existing enterprise data environment. This role is hands‑on and engineering‑driven, with a strong emphasis on dimensional modeling, data pipeline development, and modern cloud platforms including Microsoft Fabric, Azure Data Lake, and Databricks. This role will leverage Python to develop scalable, distributed data pipelines, perform data transformation and validation, and enable advanced data processing within a Lakehouse architecture.

Our major ERP source system is SAP ECC, with SAP APO as a key demand‑planning source. SAP HANA remains part of our current analytics and data warehousing environment and will continue to serve as a source system for SAP‑related data as needed. Experience with HANA is helpful but not required. Current tool stack includes SAP ECC, SAP APO, SAP HANA, SAP SLT, SAP Data Services, SQL Server, Power BI, Qlik SaaS, and SAP Business Objects.

This is a hybrid role in our corporate headquarters in suburban Philadelphia (Colmar, PA) with the expectation to be onsite two days per week.

• Build and maintain scalable ETL/ELT pipelines using Databricks, Fabric pipelines, and Python
• Develop ingestion and transformation frameworks aligned to Lakehouse and Medallion patterns, leveraging Python for distributed data processing and orchestration logic
• Implement reusable Python-based data processing modules for parsing, enrichment, and handling semi‑structured data (JSON, XML, APIs)
• Integrate data from SAP ECC, SAP APO, SAP HANA, and other enterprise systems into the modern platform
• Ensure reliability, performance, and observability across pipelines, including logging, error handling, and monitoring patterns

• Design and implement dimensional models using star schema patterns to support analytics and AI workloads
• Build and maintain semantic models (e.g., business-friendly datasets, curated subject areas, reusable metrics)
• Develop conceptual, logical, and physical data structures for enterprise reporting and data products

• Contribute to the build‑out of Azure Data Lake, Fabric, Databricks, and Delta Lake environments
• Lead engineering practices including CI/CD, Git‑based workflows, and code quality standards
• Drive adoption of notebook‑based and production‑grade Python workflows in Databricks (jobs, pipelines, reusable libraries)
• Support existing HANA‑based data models and integrations while modern cloud architecture is built and expanded

• Implement data quality checks, lineage, and metadata standards
• Develop data validation frameworks to enforce business rules, schema checks, and data quality thresholds
• Monitor and optimize pipeline and query performance
• Align engineering work with enterprise governance frameworks
• Mentor data engineers and BI/reporting developers
• Translate business needs into scalable engineering solutions
• Partner with analytics teams to enable self‑service and AI‑driven use cases

• Strong experience in dimensional modeling and semantic modeling
• Hands‑on experience with Azure Data Lake, Fabric, Databricks, Delta Lake, and Python
• Experience building ETL/ELT pipelines and working with Lakehouse/Medallion architectures
• Strong understanding of Python-based data engineering concepts, including:
• Data cleansing and transformation patterns
• Handling structured and semi‑structured data

• Familiarity with SAP ECC as a primary ERP source system
• Understanding of SAP APO demand‑planning data structures
• Exposure to HANA modeling or extraction patterns

• Power BI experience preferred
• Git Hub, CI/CD, and data governance tooling familiarity

• Bachelor’s degree in Computer Science, Information Systems, or related field
• 5+ years in data engineering / data architecture
• 3+ years in leadership or senior technical role