3 days ago
Pune, IndiaStaff+
Responsibilities
- Develop and maintain enterprise data products, data pipelines, transformations, and curated datasets.
- Implement and automate distributed data ingestion and transformation systems for relational, event-based, and unstructured sources.
- Build reliable, scalable ETL/ELT pipelines with monitoring, alerting, and data quality controls.
- Develop physical data models and implement data storage architectures using distributed and cloud-based platforms.
- Establish and maintain metadata, lineage, access, retention, governance, and discoverability processes.
- Monitor and troubleshoot data quality, data integrity, pipeline, and solution validation issues.
- Apply Data-as-a-Product principles to create reusable, governed, and self-service data assets.
- Translate business and product requirements into scalable, performant, maintainable, and reusable technical solutions.
- Collaborate with product, engineering, analytics, data science, solution engineering, and business stakeholders.
- Support AI-ready datasets, semantic models, knowledge assets, and data structures for AI/ML and GenAI applications.
- Document solutions, validate changes, write code and scripts, and contribute to requirements, testing, and quality assurance activities.
Requirements
- College or university degree in a relevant technical discipline, or relevant equivalent experience.
- Bachelor's degree in Computer Science, Information Technology, Engineering, Data Analytics, or equivalent practical experience.
- Experience developing and supporting data integration, ETL/ELT processes, data pipelines, and data transformations using modern data platforms and cloud technologies.
- Working knowledge of data modeling, SQL, data quality, metadata management, and data governance principles.
- Knowledge or exposure to Big Data technologies, Spark, Scala or Java, MapReduce, Hive, HBase, Kafka, clustered cloud computing, large-file movement, analytical solutions, and IoT technology.
- Ability to translate stakeholder and product requirements into verifiable, efficient technical solutions.
- Experience collaborating in Agile, cross-functional teams with product, engineering, analytics, and business stakeholders.
- Preferred experience with Data-as-a-Product operating models, data catalogs, lineage, data quality frameworks, and certified data products.
- Preferred exposure to AI/ML or GenAI initiatives, including AI-ready datasets, semantic models, knowledge assets, or intelligent business solutions.
- Relevant experience from temporary student employment, internships, co-ops, extracurricular team activities, or equivalent activities is preferred.
Tech Stack
Amazon DynamoDBApache CassandraApache HadoopApache HBaseApache HiveApache KafkaApache SparkJavaMongoDBScalaSQL
Categories
Data Engineering
About Cummins
Cummins Inc. designs and manufactures diesel, natural gas, and electrified powertrains, generators, and related components (turbochargers, filtration, emission systems) for commercial vehicles, industrial equipment, marine, rail, and power markets. It sells to OEMs and fleet operators and provides parts and service through a global distribution network. Founded in 1919 and headquartered in Columbus, Indiana, Cummins is a public company listed on the NYSE (CMI).