Data Engineer Principal

GPP Database Link

Job Summary:

We are looking for a talented Data Engineering Manager to join our Cummins Inc. team in Columbus, Indiana .

In this role, you will make an impact in the following ways:

- Lead the strategy, architecture, and evolution of enterprise data platforms that enable scalable analytics, AI, and business intelligence solutions.

- Partner with business leaders, product teams, and technical stakeholders to translate complex requirements into high-value data solutions.

- Design and optimize data lake, lakehouse, data warehouse, and cloud-based architectures that improve data accessibility, quality, and performance.

- Deliver resilient and reusable data pipelines that accelerate decision-making and reduce time-to-insight across the organization.

- Champion data governance, security, compliance, and quality standards to ensure trusted and reliable enterprise data assets.

- Drive continuous improvement initiatives that enhance scalability, operational efficiency, cost optimization, and platform performance.

- Provide technical leadership, mentoring, and architectural guidance to data engineering teams while fostering engineering excellence.

- Enable executive and business-critical decision making through the integration and delivery of data from diverse enterprise systems.

Additional Responsibilities & Preferred Key Competencies:

- 10+ years of progressive experience in data engineering, data architecture, analytics engineering, or a closely related technical field, including experience leading complex enterprise data solutions.
- Demonstrated depth of experience delivering enterprise data, analytics, or AI solutions within large, complex manufacturing and supply-chain environments , with experience across one or more areas such as planning, procurement, manufacturing, inventory, logistics, engineering, aftermarket, commercial, finance, or related operational functions.
- Demonstrated ability to work directly with business stakeholders to understand complex business problems and processes, clarify requirements, explore available data, and develop prototypes or proof-of-concepts that validate solution approaches before scaling successful solutions into production.
- Strong hands-on expertise in modern data engineering, including SQL, Python/PySpark, data modeling, scalable pipeline design, data integration, and distributed/cloud data platforms .
- Demonstrated experience designing, building, and operating batch and streaming or near-real-time data pipelines , with consideration for orchestration, reliability, monitoring, recovery, scalability, and performance.
- Experience integrating data across a variety of complex enterprise source systems , such as ERP and operational systems, legacy applications and databases, APIs, cloud platforms, event streams, IoT/telemetry sources, and structured or unstructured data.
- Demonstrated experience designing and evolving enterprise-scale data architectures , including data lake, lakehouse, data warehouse, or comparable modern analytical platforms.
- Strong data modeling experience, including relational, dimensional, and enterprise/domain data modeling , fact and dimension structures, star or snowflake schemas, conformed dimensions, and other appropriate modeling patterns supporting analytics, operational, and AI use cases.
- Experience building reusable data engineering frameworks, shared data foundations, enterprise data models, and governed data products that can support multiple business, analytics, AI, and operational use cases rather than a single project.
- Strong experience with modern enterprise data platforms such as Databricks, Snowflake, Azure data services, or comparable cloud/data technologies .
- Proven ability to lead solutions across the full lifecycle , from business discovery, requirements definition, and data exploration through architecture, implementation, production deployment, monitoring, optimization, and ongoing support.
- Demonstrated ability to work effectively in complex and ambiguous data environments involving multiple source systems, evolving requirements, data-quality issues, integration constraints, and competing business needs.
- Strong ability to collaborate across Business, Data Science, AI Engineering, Analytics, Enterprise Architecture, application, and platform teams to translate business needs into scalable and practical technical solutions.
- Experience providing technical leadership , including architecture guidance, design reviews, engineering standards, solution trade-off decisions, mentoring, and coaching of engineers and other technical contributors.
- Demonstrated understanding of the data engineering and architecture foundations required to enable advanced analytics, machine learning, GenAI, and other AI-enabled solutions , while maintaining appropriate standards for data quality, governance, security, scalability, reuse, performance, and cost.

Preferred Key Competencies:
- Experience designing enterprise-level analytical, operational, or domain data models spanning multiple manufacturing and supply-chain business functions and source systems.
- Experience implementing metadata-driven pipelines, reusable ingestion frameworks, self-service data capabilities, data governance, lineage, observability, or reusable data-product patterns .
- Experience with data architecture and engineering patterns supporting GenAI and RAG solutions , including document ingestion and processing, embeddings, vector search/vector databases, semantic models, knowledge graphs, ontologies, or retrieval pipelines.
- Experience working with manufacturing and supply-chain technologies and data sources such as ERP/MRP, MES, PLM, WMS, TMS, planning systems, engineering systems, or IoT/connected-product platforms .
- Demonstrated ability to balance near-term business delivery with longer-term architecture, scalability, reuse, governance, cost optimization, and technical debt .
- Relevant Databricks, Snowflake, Azure, AWS, GCP, or comparable data-platform certifications are a plus; demonstrated production experience and technical depth are valued more strongly than certification alone.

Please note that the salary range provided is a good faith estimate on the applicable range. The final salary offer will be determined after considering relevant factors, including a candidate’s qualifications and experience, where appropriate.