Data Engineer

DESCRIPTION

Job Summary:

Cummins is seeking a skilled Data Engineer to support the development, maintenance, and optimization of our enterprise data and analytics platform. This role involves hands-on experience in software development , ETL processes , and data warehousing , with strong exposure to tools like Snowflake , OBIEE , and Power BI . The engineer will collaborate with cross-functional teams, transforming data into actionable insights that enable business agility and scale.

Please Note: While the role is categorized as remote, it will follow a hybrid work model based out of our Pune office .

• Design, develop, and maintain ETL pipelines using Snowflake and related data transformation tools.
• Build and automate data integration workflows that extract, transform, and load data from various sources including Oracle EBS and other enterprise systems.
• Analyze, monitor, and troubleshoot data quality and integrity issues using standardized tools and methods.
• Develop and maintain dashboards and reports using OBIEE , Power BI , and other visualization tools for business stakeholders.
• Work with IT and Business teams to gather reporting requirements and translate them into scalable technical solutions.
• Participate in data modeling and storage architecture using star and snowflake schema designs.
• Contribute to the implementation of data governance , metadata management , and access control mechanisms .
• Maintain documentation for solutions and participate in testing and validation activities.
• Support migration and replication of data using tools such as Qlik Replicate and contribute to cloud-based data architecture .
• Apply agile and DevOps methodologies to continuously improve data delivery and quality assurance processes.

• Data Extraction & Transformation - Ability to perform ETL activities from varied sources with high data accuracy.
• Programming - Capable of writing and testing efficient code using industry standards and version control systems.
• Data Quality Management - Detect and correct data issues for better decision-making.
• Solution Documentation - Clearly document processes, models, and code for reuse and collaboration.
• Solution Validation - Test and validate changes or solutions based on customer requirements.
• Problem Solving - Address technical challenges systematically to ensure effective resolution and prevention.
• Customer Focus - Understand business requirements and deliver user-centric data solutions.
• Communication & Collaboration - Work effectively across teams to meet shared goals.
• Values Differences - Promote inclusion by valuing diverse perspectives and backgrounds. Education, Licenses, Certifications:
• Bachelor's or Master's degree in Computer Science, Information Systems, Data Engineering, or a related technical discipline.
• Certifications in data engineering or relevant tools (Snowflake, Power BI, etc.) are a plus.

• 5-7 years of experience in data engineering or software development , preferably within a finance or enterprise IT environment.
• Proficient in ETL tools , SQL , and data warehouse development .
• Proficient in Snowflake , Power BI , and OBIEE reporting platforms. Must have worked in implementation using these tools and technologies.
• Strong understanding of data warehousing principles , including schema design (star/snowflake), ER modeling, and relational databases.
• Working knowledge of Oracle databases and Oracle EBS structures.

• Experience with Qlik Replicate , data replication , or data migration tools.
• Familiarity with data governance , data quality frameworks , and metadata management .
• Exposure to cloud-based architectures, Big Data platforms (e.g., Spark, Hive, Kafka), and distributed storage systems (e.g., HBase, MongoDB).
• Understanding of agile methodologies (Scrum, Kanban) and DevOps practices for continuous delivery and improvement.

• Opportunity to work with a global leader in power solutions and digital transformation.
• Be part of a collaborative and inclusive team culture.
• Access to cutting-edge data platforms and tools.
• Exposure to enterprise-scale data challenges and finance domain expertise .
• Drive impact through data innovation and process improvement .