Data Modeler

Cummins•Indianapolis, IN

7h•$123,030 - $150,370•Remote

About The Position

We are looking for a talented Data Modeler/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 by designing and automating scalable data ingestion and transformation pipelines across relational, event-based, and unstructured sources. You will build and maintain frameworks to monitor, detect, and resolve data quality and integrity issues, and implement data governance practices, including metadata management, data access, and retention policies. You will architect and guide the development of reliable, efficient, and scalable ETL/ELT data pipelines with monitoring and alerting. You will design physical data models and optimize database structures, indexing, and relationships for performance. You will test, optimize, and troubleshoot data pipelines to ensure stability and performance. You will develop and manage large-scale data storage solutions using distributed and cloud platforms (e.g., data lakes, Hadoop, NoSQL databases). You will drive automation and modernization of data infrastructure and integration processes to support agile analytics initiatives.

Requirements

College, university, or equivalent degree in relevant technical discipline, or relevant equivalent experience required.
Intermediate experience in a relevant discipline area is required.
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
6-8 years of experience required.

Nice To Haves

Knowledge of the latest technologies and trends in data engineering are highly preferred
Experience with IoT technology
Experience in Agile software development
Dimensional Modeling Mastery — Deep expertise in designing enterprise‑scale 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 late‑arriving data handling.
Advanced SQL Engineering — Highly proficient in writing complex, high‑performance SQL, including window functions, CTE‑driven transformations, query plan analysis, cost‑based optimization, partitioning strategies, and performance tuning across large, distributed datasets.
Snowflake Architecture & Engineering — Hands‑on experience with Snowflake internals including micro‑partitioning, clustering keys, result‑set caching layers, warehouse sizing/auto‑suspend tuning, Snowpipe/Streams/Tasks orchestration, Time Travel, Zero‑Copy 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, KQL‑based Real‑Time 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/ODP‑based extraction patterns, and harmonization of SAP transactional data into cloud‑based analytical platforms.
Cloud Data Architecture — Strong understanding of distributed data processing, ELT/ETL orchestration, event‑driven ingestion (Kafka/Event Hub), metadata‑driven 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, row‑level and column‑level security).
Performance Engineering & Optimization — Ability to diagnose bottlenecks across compute, storage, and network layers; optimize workloads for cost and performance; and design scalable, fault‑tolerant data architectures.
Cross‑Platform 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.

Responsibilities

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