3 days ago
Pune, IndiaStaff+
Responsibilities
- Develop and maintain enterprise data products, data pipelines, data transformations, and curated datasets supporting reporting, analytics, automation, machine learning, and GenAI use cases.
- Implement and automate distributed data ingestion and transformation systems for relational, event-based, and unstructured data sources.
- Build reliable, scalable data pipelines with monitoring, alerting, testing, and troubleshooting capabilities.
- Develop physical data models and implement data storage architectures using distributed and cloud-based platforms.
- Implement data governance processes covering metadata, lineage, access, retention, data quality, and data integrity.
- Analyze data elements, systems, data flows, dependencies, and relationships to support conceptual, logical, and physical data models.
- Apply Data-as-a-Product principles to create reusable, discoverable, 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 in cross-functional teams.
Requirements
- College, university, or equivalent 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 with modern data platforms and cloud-based technologies.
- Working knowledge of data modeling, SQL, data quality practices, metadata management, and data governance principles.
- Ability to translate business and product requirements into efficient technical solutions while considering scalability, performance, maintainability, and reusability.
- Experience working in Agile, cross-functional teams and collaborating with product, engineering, analytics, and business stakeholders.
- Preferred exposure to Big Data open source technologies, Spark, Scala or Java, MapReduce, Hive, HBase, Kafka, clustered cloud computing, cloud-based large-file movement, analytical solutions, and IoT technology.
- Preferred experience with Data-as-a-Product models, data catalogs, lineage, data quality frameworks, certified data products, AI/ML initiatives, GenAI initiatives, AI-ready datasets, semantic models, or knowledge assets.
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).