Data Engineer

About This Role

Dow has an exciting opportunity for a Data Engineer located in Midland, MI or Houston, TX or Champaign, IL (Dow Delivery Center at UIUC). This role will make significant technical contributions to critical data initiatives within our team at Dow. You will be responsible for driving the technical implementation and contributing to the design of scalable, Gold-layer data products on the Azure Databricks Lakehouse Platform

.
This role focuses on solving complex technical challenges, optimization, architecture contribution, and reliability, ensuring our datasets are performant and ready to power advanced use cases, includin

• g:Machine Learning (ML) Pipelin
• esReal-Time Data Consumpti
• onGenerative and Agentic AI Syste
• msCore Enterprise Reporting and
• BIData-driven Applicatio

ns
Responsibilit

• iesTechnical Design Contribution: Collaborate with senior data engineers to translate complex business requirements and ambiguous problem statements into clear, robust, and scalable technical designs and data models (e.g., dimensional modeling, star schemas), and independently drive the implementation of these desig
• ns.Performance Optimization: Design, build, and deploy high-volume data transformation logic using highly optimized PySpark. You will apply advanced techniques to tune Spark jobs and diagnose performance bottlenecks to ensure maximum efficiency and minimal cloud compute co
• st.Architecture & Deployment: Contribute significantly to the design and improvement of CI/CD pipelines in Azure DevOps/Git, ensuring reliable, automated, and secure deployment of data solutions across environmen
• ts.Diverse Data Integration: Deeply understand and connect to various source systems, demonstrating proficiency in managing data persistence and query performance across diverse technologies like SQL Server, Neo4j, and Cosmos
• DB.Quality & Governance: Proactively implement and maintain advanced data quality frameworks (e.g., Delta Live Tables, Great Expectations) and monitoring solutions to ensure data reliability for mission-critical applicatio
• ns.Collaboration & Mentorship: Serve as a go-to technical resource for peers, conducting technical code reviews and informally mentoring Associate Data Engineers on PySpark and Databricks best practic

es.
A successful candidate will possess the experience and technical depth required to independently implement and optimize complex data soluti

• ons:Core Technical Expertise (2-5 Years Demonstrated Experie
• nce)PySpark and Distributed Processing: Proven ability to write highly optimized, production-grade PySpark/Spark code. Experience identifying and resolving performance bottlenecks in a distributed computing environm
• ent.Advanced Data Modeling: Practical experience designing and implementing analytical data models (e.g., dimensional modeling, star/snowflake schemas) and handling Slowly Changing Dimensions (SC
• Ds).Cloud Orchestration: Expertise in using Azure Data Factory (ADF), Databricks Workflows, or equivalent tools (e.g., Airflow) for complex dependency management, error handling, and end-to-end pipeline orchestrat
• ion.Database Versatility: Demonstrated experience with advanced SQL and hands-on experience querying and integrating data from at least one non-relational or Graph database (e.g., CosmosDB, Neo
• 4j).Engineering Mindset and Professional Gr
• owthTechnical Design Contribution: Ability to rapidly synthesize information and contribute clear, well-documented technical specifications and architectural diagrams to the design proc
• ess.Feature Ownership: Demonstrated history of taking ownership of complex features and modules within larger projects, driving them to completion, and managing technical dependencies autonomou
• sly.Pragmatism and Initiative: A strong bias for action, coupled with a pragmatic approach to delivering stable, maintainable, and cost-effective soluti
• ons.Communication & Influence: Excellent verbal and written communication skills, with the ability to articulate technical designs to both engineering peers and senior stakeholders, effectively influencing technical decisi

ons.
Required Qualifica

• tionsA minimum of a bachelor’s degree or relevant military experience at or above a U.S. E5 ranking or Canadian Petty Officer 2nd Class or Sergeant OR 5 years relevant experience in lieu of a Bachelor’s de
• gree.Minimum of 2 years of professional experience in Data Engineering, Software Engineering, or a closely related f
• ield.Minimum of 2 years of hands-on experience with Databricks Plat
• form.A minimum requirement for this U.S. based position is the ability to work legally in the United States. No visa sponsorship/support is available for this position, including for any type of U.S. permanent residency (green card) pro

cess.
Preferred

• SkillsExperience with cloud cost management principles related to compute (Databricks) and storage (
• ADLS).Experience with Infrastructure as Code (e.g., Terraform, ARM templ
• ates).Proficiency with data visualization and dashboarding tools (e.g., Power BI, Tab

leau).
Your

• SkillsPySpark / Distributed Data Processing: The ability to build, optimize, and troubleshoot high‑volume data transformation pipelines using PySpark, including tuning Spark jobs, resolving performance bottlenecks, and ensuring efficient distributed exe
• cution.Advanced Data Modeling (Dimensional / Star Schema Design): Expertise in translating complex business requirements into scalable analytical data models—such as star and snowflake schemas—and implementing SCD logic for downstream ana
• lytics.Cloud Orchestration & CI/CD (Azure Data Factory, Databricks Workflows, Azure DevOps/Git): Skill in designing automated, reliable data pipelines, managing task dependencies, and implementing CI/CD deployment processes across enviro
• nments.Data Integration Across Diverse Systems (SQL Server, CosmosDB, Neo4j): Ability to connect to, query, and integrate data from relational and non‑relational sources while optimizing persistence, ingestion, and query perfo
• rmance.Data Quality Engineering & Governance (Delta Live Tables, Great Expectations): Applying validation frameworks, monitoring, and automated quality checks to ensure data reliability for ML, real‑time analytics, and enterprise BI use

cases.
Note: relocation assistance is not provided with th