2 months ago
Pune, IndiaSenior
Responsibilities
- Lead enterprise data model and data product design, governance, review, and continuous evolution.
- Design and implement scalable data ingestion, transformation, integration, orchestration, storage, and processing pipelines.
- Create conceptual, logical, physical, dimensional, semantic, normalized, canonical, and Data Vault models.
- Review source integrations, entities, attributes, relationships, and model changes for architectural alignment, scalability, reusability, and maintainability.
- Implement data quality monitoring, governance, metadata, access, retention, lineage, and security practices.
- Optimize database performance through indexing and table relationships.
- Build governed, discoverable, reusable, and AI-ready data products across enterprise domains.
- Partner with engineers, architects, analysts, solution engineers, and business stakeholders to translate requirements into data solutions.
- Test, troubleshoot, document, and validate data pipelines and solutions.
- Coach and develop less experienced team members.
Requirements
- Bachelor's degree in Computer Science, Information Technology, Data Analytics, Engineering, Information Systems, or a related field; a master's degree is preferred.
- 8+ years of experience in enterprise data modeling and data engineering supporting large-scale warehouses, lakehouses, semantic models, and enterprise data products.
- Strong expertise in enterprise data modeling, dimensional modeling, Data Vault, normalized and canonical models, semantic modeling, master data management, and enterprise data architecture standards.
- Experience governing and reviewing enterprise data models across multiple data products and source systems.
- Hands-on experience designing data solutions across source, raw/bronze, curated/silver, business/gold, semantic, and enterprise data product layers.
- Strong hands-on experience with Snowflake, Databricks, SQL, Matillion, dbt, and Power BI.
- Experience building scalable data pipelines, data integration solutions, reusable enterprise data assets, and cloud-based data platforms.
- Experience with large datasets, Big Data platforms, data warehouses, lakehouses, cloud-based clustered computing, and large file movement.
- Experience or coursework involving Spark, Scala or Java, MapReduce, Hive, HBase, and Kafka.
- Knowledge of data regulations, complex business systems, data quality, data governance, analytics solutions, and data extraction methods.
- Preferred experience with Data-as-a-Product operating models, semantic layers, data cataloging, metadata management, data lineage, certification, and self-service analytics.
- Preferred experience with AI-ready data platforms, GenAI, AI/ML, retrieval-augmented generation, vector databases, semantic search, and knowledge management.
- Experience with IoT technology and Agile software development is preferred.
- Relevant equivalent experience may substitute for the required degree.
Tech Stack
Amazon DynamoDBApache CassandraApache HadoopApache HBaseApache HiveApache KafkaApache SparkDatabricksdbtJavaMongoDBScalaSnowflakeSQL
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).