Data Engineer Principal

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.

• Proven ability to architect and deliver enterprise-scale data platforms, data models, and cloud-based analytics solutions that support business growth and innovation.
• Deep expertise in modern data engineering practices, including scalable pipeline development, data integration, data modeling, distributed processing, and cloud-native architectures.
• Strong leadership and stakeholder management skills with the ability to influence cross-functional teams, navigate ambiguity, and align technology solutions with business outcomes.
• Advanced knowledge of data governance, security, compliance, and modern software engineering practices, including Agile, DevSecOps, CI/CD, and automation.
• Demonstrated passion for innovation, continuous learning, and leveraging emerging technologies to drive measurable business value.

Education, Licenses, Certifications:

College, university, or equivalent degree in relevant technical discipline, or relevant equivalent experience required. This position may require licensing for compliance with export controls or sanctions regulations.

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.