Data Engineer Principal

Cummins•Columbus, IN

19h•$131,220 - $160,380•Onsite

About The Position

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.

Requirements

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.
College, university, or equivalent degree in relevant technical discipline, or relevant equivalent experience required.
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.

Nice To Haves

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.

Responsibilities

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.