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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.

Education/Experience:

• College, university, or equivalent degree in relevant technical discipline, or relevant equivalent experience required.
• This position may require licensing for compliance with export controls or sanctions regulations.
• Intermediate experience in a relevant discipline area is required. Knowledge of the latest technologies and trends in data engineering are highly preferred and includes:
  - Familiarity analyzing complex business systems, industry requirements, and/or data regulations
  - Background in processing and managing large data sets
  - Design and development for a Big Data platform using open source and third-party tools
  - SPARK, Scala/Java, Map-Reduce, Hive, Hbase, and Kafka or equivalent college coursework
  - SQL query language
  - Clustered compute cloud-based implementation experience
  - Experience developing applications requiring large file movement for a Cloud-based environment and other data extraction tools and methods from a variety of sources
  - Experience in building analytical solutions
  Intermediate experiences in the following are preferred:
  - Experience with IoT technology
  - Experience in Agile software development
• 6-8 years of experience required.

Additional Responsibilities:

Preferred Job Specific Skills - Data Architect

• Dimensional Modeling Mastery - Deep expertise in designing enterprisescale dimensional models (star, snowflake, constellation) with strong command of fact table grain definition, surrogate key strategies, slowly changing dimensions (Types 1-6), bridge tables, and latearriving data handling.
• Advanced SQL Engineering - Highly proficient in writing complex, highperformance SQL, including window functions, CTEdriven transformations, query plan analysis, costbased optimization, partitioning strategies, and performance tuning across large, distributed datasets.
• Snowflake Architecture & Engineering - Handson experience with Snowflake internals including micropartitioning, clustering keys, resultset caching layers, warehouse sizing/autosuspend tuning, Snowpipe/Streams/Tasks orchestration, Time Travel, ZeroCopy Cloning, and secure data sharing patterns.
• Graph Database & Cypher Proficiency - Strong experience with Neo4j or equivalent graph platforms, including graph schema design, Cypher query optimization, graph algorithms (PageRank, community detection, pathfinding), and integration of graph workloads with analytical and relational systems.
• Microsoft Fabric Ecosystem - Practical experience with Fabric Lakehouse architecture, Delta Lake optimization, Data Engineering pipelines, Data Factory orchestration, KQLbased RealTime Analytics, semantic model creation, and integration with Power BI and OneLake governance.
• SAP S/4HANA Data Structures -Familiarity of SAP S/4HANA data models (FI/CO, MM, SD, PP), CDS views, OData services, SLT/SDI/ODPbased extraction patterns, and harmonization of SAP transactional data into cloudbased analytical platforms.
• Cloud Data Architecture - Strong understanding of distributed data processing, ELT/ETL orchestration, eventdriven ingestion (Kafka/Event Hub), metadatadriven frameworks, schema evolution, and data lifecycle management across cloud environments (Azure preferred).
• Data Governance & Metadata Management - Experience implementing enterprise data catalogs, lineage tracking, data quality rules, master data integration, and security models (RBAC/ABAC, rowlevel and columnlevel security).
• Performance Engineering & Optimization - Ability to diagnose bottlenecks across compute, storage, and network layers; optimize workloads for cost and performance; and design scalable, faulttolerant data architectures.
• CrossPlatform Integration - Experience integrating heterogeneous systems (SAP, Snowflake, Fabric, graph DBs, APIs, streaming platforms) into unified analytical ecosystems with strong focus on interoperability and data consistency.

Compensation:

Please note that the salary range provided is a good faith estimate on the applicable range. The final salary offer will be determined after considering relevant factors, including a candidate's qualifications and experience, where appropriate.

Premium Range:

Minimum: $123,030

Maximum: $150,370

To be successful in this role you will need the following:

• Data Extraction - Build scalable, automated ETL pipelines that deliver accurate, timely data. Choose the right tools and optimize transformations for performance and usability.
• Programming - Write clean, well-documented, and testable code using best practices. Leverage version control and automation to ensure reliability and efficiency
• Solution Validation Testing - Follow SDLC standards to thoroughly test and validate all solutions. Ensure outputs meet business requirements and perform correctly in production.
• Data Quality - Proactively monitor and resolve data issues. Establish strong governance practices to maintain data accuracy and trust across the organization.