Data Engineer - Senior

Job Description

Description GPP Database Link (https://cummins365.sharepoint.com/sites/CS38534/) Job Summary Leads projects for the design, development, and maintenance of a data and analytics platform. Effectively and efficiently processes, stores, and makes data available to analysts and other consumers. Works with key business stakeholders, IT experts, and subject-matter experts to plan, design, and deliver optimal analytics and data science solutions. Works on one or many product teams at a time. Though the role category is generally listed as Remote, this specific position is designated as Hybrid. Key Responsibilities Business Alignment & Collaboration – Partner with the Product Owner to align data solutions with strategic goals and business requirements. Data Pipeline Development & Management – Design, develop, test, and deploy scalable data pipelines for efficient data transport into Cummins Digital Core (Azure DataLake, Snowflake) from various sources (ERP, CRM, relational, event-based, unstructured). Architecture & Standardization – Ensure compliance with AAI Digital Core and AAI Solutions Architecture standards for data pipeline design and implementation. Automation & Optimization – Design and automate distributed data ingestion and transformation systems, integrating ETL/ELT tools and scripting languages to ensure scalability, efficiency, and quality. Data Quality & Governance – Implement data governance processes, including metadata management, access control, and retention policies, while continuously monitoring and troubleshooting data integrity issues. Performance & Storage Optimization – Develop and implement physical data models, optimize database performance (indexing, table relationships), and operate large-scale distributed/cloud-based storage solutions (Data Lakes, Hadoop, HBase, Cassandra, MongoDB, Accumulo, DynamoDB). Innovation & Tool Evaluation – Conduct proof-of-concept (POC) initiatives, evaluate new data tools, and provide recommendations for improvements in data management and integration. Documentation & Best Practices – Maintain standard operating procedures (SOPs) and data engineering documentation to support consistency and efficiency. Agile Development & Automation – Use Agile methodologies (DevOps, Scrum, Kanban) to drive automation in data integration, preparation, and infrastructure management, reducing manual effort and errors. Coaching & Team Development – Provide guidance and mentorship to junior team members, fostering skill development and knowledge sharing. Responsibilities Competencies: System Requirements Engineering: Translates stakeholder needs into verifiable requirements, tracks status, and assesses impact changes. Collaborates: Builds partnerships and works collaboratively with others to meet shared objectives. Communicates Effectively: Delivers multi-mode communications tailored to different audiences. Customer Focus: Builds strong customer relationships and provides customer-centric solutions. Decision Quality: Makes good and timely decisions that drive the organization forward. Data Extraction: Performs ETL activities from various sources using appropriate tools and technologies. Programming: Develops, tests, and maintains code using industry standards, version control, and automation tools. Quality Assurance Metrics: Measures and assesses solution effectiveness using IT Operating Model (ITOM) standards. Solution Documentation: Documents knowledge gained and communicates solutions for improved productivity. Solution Validation Testing: Validates configurations and solutions to meet customer requirements using SDLC best practices. Data Quality: Identifies, corrects, and manages data flaws to support effective governance and decision-making. Problem Solving: Uses systematic analysis to determine root causes and implement robust solutions. Values Differences: Recognizes and leverages the value of diverse perspectives and cultures. Education, Licenses, Certifications Bachelor's degree in a relevant technical discipline, or equivalent experience required. This position may require licensing for compliance with export controls or sanctions regulations. Qualifications Preferred Experience: Technical Expertise – Intermediate experience in data engineering with hands-on knowledge of SPARK, Scala/Java, MapReduce, Hive, HBase, Kafka, and SQL. Big Data & Cloud Solutions – Proven ability to design and develop Big Data platforms, manage large datasets, and implement clustered compute solutions in cloud environments. Data Processing & Movement – Experience developing applications requiring large-scale file movement and utilizing various data extraction tools in cloud-based environments. Business & Industry Knowledge – Familiarity with analyzing complex business systems, industry requirements, and data regulations to ensure compliance and efficiency. Analytical & IoT Solutions – Experience building analytical solutions with exposure to IoT technology and its integration into data engineering processes. Agile Development – Strong understanding of Agile methodologies, including Scrum and Kanban, for iterative development and deployment. Technology Trends – Awareness of emerging technologies and trends in data engineering, with a proactive approach to innovation and continuous learning. Technical Skills Programming Languages: Proficiency in Python, Java, and/or Scala. Database Management: Expertise in SQL and NoSQL databases. Big Data Technologies: Hands-on experience with Hadoop, Spark, Kafka, and similar frameworks. Cloud Services: Experience with Azure, Databricks, and AWS platforms. ETL Processes: Strong understanding of Extract, Transform, Load (ETL) processes. Data Replication: Working knowledge of replication technologies like Qlik Replicate is a plus. API Integration: Experience working with APIs to consume data from ERP and CRM systems. Job Systems/Information Technology Organization Cummins Inc. Role Category Remote Job Type Exempt - Experienced ReqID 2410681 Relocation Package No Show more Show less