Data Engineer - Senior

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 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.

• 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.

• 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.

• 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.

• 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.