Data Engineering Lead

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Dorman was founded on the belief that people should have greater freedom to fix motor vehicles. For over 100 years, we have been driving new solutions, releasing tens of thousands of aftermarket replacement products engineered to save time and money, and increase convenience and reliability. Founded and headquartered in the United States, we are a pioneering global organization offering an always-evolving catalog of automotive, heavy-duty and specialty vehicle products.

Today, we have more than 3,500 employees across 29 different locations, with a family of brands that also includes Super

ATV and Dayton Parts. Publicly traded under the stock ticker DORM, reaching a revenue of $2 billion in 2024.

Everyone who works at Dorman is called a Contributor. We need everyone, regardless of role and experience, to contribute to our success. That means everyone has a unique ability to make an impact on the business. We encourage all our Contributors to bring their authentic selves to work. The freedom to pursue new ideas, offer different perspectives, and grow in your career is fundamental to working at Dorman.

Dorman Products is seeking a seasoned Data Engineering Lead to architect, build, and optimize the enterprise data ecosystem. This role is ideal for a hands-on leader with deep expertise in data modeling, cloud platforms, and SAP HANA, who thrives in a collaborative environment and is passionate about driving data modernization. This is a hybrid role in our corporate headquarters in suburban Philadelphia (Colmar, PA) with the expectation to be onsite two days per week.

• Design and implement end-to-end data warehousing solutions using dimensional modeling methodologies.
• Develop and maintain enterprise data models (conceptual, logical, and physical) to support analytical and operational reporting.
• Optimize SAP HANA models/views (Calculation, CDS, and Standard SQL) for performance and reusability.
• Define and enforce modeling standards, naming conventions, and best practices across projects.
• Integrate data and semantic models with Microsoft Azure analytics service
• Oversee the development and support of ETL processes and data integration solutions.
• Partner with ETL and reporting developers to architect scalable, high-performing solutions.
• Support SLT replication setup to ensure real-time data synchronization with SAP ECC.
• Manage and optimize cloud-based data platforms.
• Collaborate with SAP administration teams on HANA upgrades, migrations, and environment refreshes.
• Use Git Hub for version control and deployment coordination of HANA objects.

Data Quality & Performance:

• Identify and resolve database performance and scalability issues.
• Integrate data quality tools and maintain detailed data lineage and source-to-target mappings.
• Review technical designs to ensure alignment with architectural standards.
• Support the Quality Assurance team with testing efforts.
• Lead and mentor a team of data engineers, fostering a culture of technical excellence and continuous improvement.
• Drive modernization of BI infrastructure by evaluating and adopting emerging technologies.
• Implement the strategic vision for data engineering and BI platforms.

Stakeholder

Collaboration:

• Work closely with product managers, business analysts, and development teams to translate business requirements into sound data architecture.
• Communicate complex architectural concepts to both technical and non-technical audiences.
• Participate in data governance and architecture review meetings to ensure consistency and compliance.

• Strong hands-on experience with SAP HANA and other relational databases (e.g., SQL Server)
• Expertise in relational and dimensional modeling (ER models, star/snowflake schemas)
• Familiarity with ETL tools and frameworks (e.g., SAP Data Services, SLT)
• Working knowledge of reporting/analytics tools (e.g., SAP Business Objects, Qlik, Power BI)
• Understanding of SAP ECC and SAP SCM/APO data structures and integration methods
• Experience with Microsoft Azure analytics services – Data Fabric, Data Lake, Delta Lake, Databricks
• Experience with Git Hub for version control
• Knowledge of…