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Data Architect at CUMMINGS INC

Indianapolis, Indiana, United States -

Full Time

Start Date

Immediate

Expiry Date

22 Sep, 26

Salary

150370.0

Posted On

24 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Dimensional Modeling, Advanced SQL, Snowflake Architecture, Graph Databases, Microsoft Fabric, SAP S/4HANA, Cloud Data Architecture, Data Governance, Performance Engineering, ETL/ELT Pipelines, Spark, Kafka, NoSQL, Data Lake, Python, Scala

Industry

Motor Vehicle Manufacturing

Description

We are looking for a talented Data Architect to join our team specializing in Systems/Information Technology for Cummins, Inc. as part of DBU Data & Analytics, Remote. In this role, you will make an impact in the following ways: * Design and automate scalable data ingestion and transformation pipelines across relational, event-based, and unstructured sources. * Build and maintain frameworks to monitor, detect, and resolve data quality and integrity issues. Implement data governance practices, including metadata management, data access, and retention policies. * Architect and guide development of reliable, efficient, and scalable ETL/ELT data pipelines with monitoring and alerting. * Design physical data models and optimize database structures, indexing, and relationships for performance. * Test, optimize, and troubleshoot data pipelines to ensure stability and performance. * Develop and manage large-scale data storage solutions using distributed and cloud platforms (e.g., data lakes, Hadoop, NoSQL databases). * Drive automation and modernization of data infrastructure and integration processes to support agile analytics initiatives. Cummins is an equal opportunity employer. Our policy is to provide equal employment opportunities to all qualified persons without regard to race, sex, color, disability, national origin, age, religion, union affiliation, sexual orientation, veteran status, citizenship, gender identity, or other status protected by law.

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Responsibilities

Design and automate scalable data ingestion and transformation pipelines across various data sources. Architect reliable ETL/ELT frameworks and optimize physical data models for high-performance cloud storage solutions.