Data Engineer Principal

Lead enterprise data platform strategy and engineering to support scalable analytics, AI, and business intelligence for Cummins in Columbus, Indiana.

Responsibilities

• Own the strategy, architecture, and ongoing evolution of enterprise data platforms enabling analytics, AI, and business intelligence.
• 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 to improve accessibility, data quality, and performance.
• Deliver resilient, reusable data pipelines that reduce time-to-insight and accelerate enterprise decision-making.
• Establish and champion data governance, security, compliance, and quality standards for trusted enterprise data assets.
• Drive continuous improvement for scalability, operational efficiency, cost optimization, and platform performance.
• Provide technical leadership, mentoring, and architectural guidance to data engineering teams while promoting engineering excellence.
• Enable executive and business-critical decisions by integrating and delivering data from diverse enterprise systems.

Requirements

• Proven experience architecting and delivering 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 to influence cross-functional teams, handle ambiguity, and align technology decisions with business outcomes.
• Advanced knowledge of data governance, security, compliance, and modern software engineering practices, including Agile, DevSecOps, CI/CD, and automation.
• 10+ years of progressive experience in data engineering, data architecture, analytics engineering, or a closely related technical field, including leadership of complex enterprise data solutions.
• Demonstrated delivery experience across large, complex manufacturing and supply-chain environments, with exposure to areas such as planning, procurement, manufacturing, inventory, logistics, engineering, aftermarket, commercial, finance, or related operational functions.
• Ability to work directly with business stakeholders to understand complex problems and processes, clarify requirements, explore available data, and validate approaches using prototypes or proof-of-concepts before scaling to production.
• Hands-on expertise with SQL and Python/PySpark, including data modeling, scalable pipeline design, data integration, and distributed or cloud data platforms.
• Experience building and operating batch and streaming or near-real-time pipelines, including orchestration, reliability, monitoring, recovery, scalability, and performance.
• Experience integrating data across complex enterprise sources such as ERP and operational systems, legacy apps and databases, APIs, cloud platforms, event streams, and IoT or telemetry sources, including structured or unstructured data.
• Demonstrated experience designing and evolving enterprise-scale analytical architectures including data lake, lakehouse, data warehouse, or comparable platforms.
• Strong data modeling experience across relational, dimensional, and enterprise/domain modeling, including fact and dimension structures, star/snowflake schemas, conformed dimensions, and patterns supporting analytics, operational, and AI use cases.
• Experience creating reusable data engineering frameworks, shared foundations, enterprise data models, and governed data products supporting multiple use cases.
• Experience with modern enterprise data platforms such as Databricks, Snowflake, and Azure data services or comparable cloud/data technologies.
• Proven ability to lead full lifecycle delivery: discovery and requirements, data exploration, architecture, implementation, production deployment, monitoring, optimization, and ongoing support.
• Ability to collaborate across Business, Data Science, AI Engineering, Analytics, Enterprise Architecture, and application and platform teams to deliver scalable technical solutions.
• Experience providing technical leadership: architecture guidance, design reviews, engineering standards, solution trade-off decisions, and mentoring/coaching engineers.
• Understanding of data engineering and architecture foundations needed to support advanced analytics, machine learning, GenAI, and AI-enabled solutions while maintaining standards for quality, governance, security, scalability, reuse, performance, and cost.

Technologies

• SQL
• Python
• PySpark
• Agile
• DevSecOps
• CI/CD
• Data lake
• Lakehouse
• Data warehouse
• Databricks
• Snowflake
• Azure data services
• ERP
• APIs
• Event streams
• IoT/telemetry sources
• Batch and streaming data pipelines
• Near-real-time data pipelines
• Streaming
• Distributed processing
• Cloud-native architectures

Education / Licenses / Certifications

• College, university, or equivalent degree in a 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

• Experience designing enterprise-level analytical, operational, or domain data models across 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, including document ingestion and processing, embeddings, vector search/vector databases, semantic models, knowledge graphs, ontologies, or retrieval pipelines.
• Experience 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.
• 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; production experience and technical depth are valued more than certification alone.

Relocation

• Relocation package: Yes

Location: Columbus, IN (onsite) | Role category: On-site with Flexibility | Job type: Exempt - Experienced

Salary: USD 131,220 - 160,380 per yearly | Min salary: $131220 | Max salary: $160380 | REQID: 2438266

Note: Salary range estimate provided in good faith; final offer determined based on qualifications and experience, where appropriate.