Data Engineer

DESCRIPTION

• Product & Business Alignment - Collaborate with the Product Owner to align data solutions with business objectives and product vision.
• Data Pipeline Development - Design, develop, and implement efficient data pipelines for ingesting, transforming, and transporting data into Cummins Digital Core (Azure DataLake, Snowflake) from various sources, including transactional systems (ERP, CRM).
• Architecture & Standards Compliance - Ensure alignment with AAI Digital Core and AAI Solutions Architecture standards for data pipeline design, storage architectures, and governance processes.
• Automation & Optimization - Implement and automate distributed data systems, ensuring reliability, scalability, and efficiency through monitoring, alerting, and performance tuning.
• Data Quality & Governance - Develop and enforce data governance policies, including metadata management, access control, and retention policies, while actively monitoring and troubleshooting data quality issues.
• Modeling & Storage - Design and implement conceptual, logical, and physical data models, optimizing storage architectures using distributed and cloud-based platforms (e.g., Hadoop, HBase, Cassandra, MongoDB, Accumulo, DynamoDB).
• Documentation & Best Practices - Create and maintain data engineering documentation, including standard operating procedures (SOPs) and best practices, with guidance from senior engineers.
• Tool Evaluation & Innovation - Support proof-of-concept (POC) initiatives and evaluate emerging data tools and technologies to enhance efficiency and effectiveness.
• Testing & Troubleshooting - Participate in the testing, troubleshooting, and continuous improvement of data pipelines to ensure data integrity and usability.
• Agile & DevOps Practices - Utilize agile development methodologies, including DevOps, Scrum, and Kanban, to drive iterative improvements in data-driven applications.

• 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.

• System Requirements Engineering: Translate stakeholder needs into verifiable requirements, establish acceptance criteria, track requirement status, and assess impact changes.
• Collaborates: Build partnerships and work collaboratively with others to meet shared objectives.
• Communicates Effectively: Deliver multi-mode communications tailored to different audiences.
• Customer Focus: Build strong customer relationships and provide customer-centric solutions.
• Decision Quality: Make good and timely decisions that drive the organization forward.
• Data Extraction: Perform ETL activities from various sources using appropriate tools and technologies.
• Programming: Develop, test, and maintain code using industry standards, version control, and automation tools.
• Quality Assurance Metrics: Measure and assess solution effectiveness using IT Operating Model (ITOM) standards.
• Solution Documentation: Document knowledge gained and communicate solutions for improved productivity.
• Solution Validation Testing: Validate configurations and solutions to meet customer requirements using SDLC best practices.
• Data Quality: Identify, correct, and manage data flaws to support effective governance and decision-making.
• Problem Solving: Use systematic analysis to determine root causes and implement robust solutions.
• Values Differences: Recognize and leverage the value of diverse perspectives and cultures.

• Hands-on experience gained through internships, co-ops, student employment, or team-based extracurricular projects.
• Proficiency in SQL query language and experience in developing analytical solutions.
• Exposure to open-source Big Data technologies such as Spark, Scala/Java, MapReduce, Hive, HBase, and Kafka.
• Familiarity with cloud-based, clustered computing environments and large-scale data movement applications.
• Understanding of Agile software development methodologies.
• Exposure to IoT technology and data-driven solutions.

1. Programming Languages: Proficiency in Python, Java, and/or Scala.
2. Database Management: Expertise in SQL and NoSQL databases.
3. Big Data Technologies: Hands-on experience with Hadoop, Spark, Kafka, and similar frameworks.
4. Cloud Services: Experience with Azure, Databricks, and AWS platforms.
5. ETL Processes: Strong understanding of Extract, Transform, Load (ETL) processes.
6. Data Replication: Working knowledge of replication technologies like Qlik Replicate is a plus.
7. API Integration: Experience working with APIs to consume data from ERP and CRM systems.