IT Data Engineer

Dow•Midland, MI

About The Position

At Dow, we believe in putting people first and we’re passionate about delivering integrity, respect and safety to our customers, our employees and the planet. Our people are at the heart of our solutions. They reflect the communities we live in and the world where we do business. Their diversity is our strength. We’re a community of relentless problem solvers that offers the daily opportunity to contribute with your perspective, transform industries and shape the future. Our purpose is simple - to deliver a sustainable future for the world through science and collaboration. If you’re looking for a challenge and meaningful role, you’re in the right place. Dow (NYSE: DOW) is one of the world’s leading materials science companies, serving customers in high-growth markets such as packaging, infrastructure, mobility and consumer applications. Our global breadth, asset integration and scale, focused innovation, leading business positions and commitment to sustainability enable us to achieve profitable growth and help deliver a sustainable future. We operate manufacturing sites in 30 countries and employ approximately 36,000 people. Dow delivered sales of approximately $43 billion in 2024. References to Dow or the Company mean Dow Inc. and its subsidiaries. Learn more about us and our ambition to be the most innovative, customer-centric, inclusive and sustainable materials science company in the world by visiting www.dow.com. About you and this role Dow has an exciting opportunity for a Data Engineer located in Midland, MI or Houston, TX. 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, including: Machine Learning (ML) Pipelines Real-Time Data Consumption Generative and Agentic AI Systems Core Enterprise Reporting and BI Data-driven Applications Responsibilities Technical 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 designs. 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 cost. 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 environments. 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 CosmosDB. 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 applications. 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 practices. A successful candidate will possess the experience and technical depth required to independently implement and optimize complex data solutions: Core Technical Expertise (2-5 Years Demonstrated Experience) 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 environment. Advanced Data Modeling: Practical experience designing and implementing analytical data models (e.g., dimensional modeling, star/snowflake schemas) and handling Slowly Changing Dimensions (SCDs). 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 orchestration. 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, Neo4j). Engineering Mindset and Professional Growth Technical Design Contribution: Ability to rapidly synthesize information and contribute clear, well-documented technical specifications and architectural diagrams to the design process. Feature Ownership: Demonstrated history of taking ownership of complex features and modules within larger projects, driving them to completion, and managing technical dependencies autonomously. Pragmatism and Initiative: A strong bias for action, coupled with a pragmatic approach to delivering stable, maintainable, and cost-effective solutions. 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 decisions.

Requirements

A 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 degree.
Minimum of 2 years of professional experience in Data Engineering, Software Engineering, or a closely related field.
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) process.
Proven ability to write highly optimized, production-grade PySpark/Spark code. Experience identifying and resolving performance bottlenecks in a distributed computing environment.
Practical experience designing and implementing analytical data models (e.g., dimensional modeling, star/snowflake schemas) and handling Slowly Changing Dimensions (SCDs).
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 orchestration.
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, Neo4j).
Ability to rapidly synthesize information and contribute clear, well-documented technical specifications and architectural diagrams to the design process.
Demonstrated history of taking ownership of complex features and modules within larger projects, driving them to completion, and managing technical dependencies autonomously.
A strong bias for action, coupled with a pragmatic approach to delivering stable, maintainable, and cost-effective solutions.
Excellent verbal and written communication skills, with the ability to articulate technical designs to both engineering peers and senior stakeholders, effectively influencing technical decisions.

Nice To Haves

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

Responsibilities

Technical 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 designs.
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 cost.
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 environments.
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 CosmosDB.
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 applications.
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 practices.

Benefits

Equitable and market-competitive base pay and bonus opportunity across our global markets, along with locally relevant incentives.
Benefits and programs to support your physical, mental, financial, and social well-being, to help you get the care you need...when you need it.
Competitive retirement program that may include company-provided benefits, savings opportunities, financial planning, and educational resources to help you achieve your long term financial-goals.
Employee stock purchase programs (availability varies depending on location).
Student Debt Retirement Savings Match Program (U.S. only). Dow will take the value of monthly student debt payments and apply them as if they are contributions to the Employees’ Savings Plan (401(k)), helping employees reach the Company match.
Robust medical and life insurance packages that offer a variety of coverage options to meet your individual needs. Travel insurance is also available in certain countries/locations.
Opportunities to learn and grow through training and mentoring, work experiences, community involvement and team building.
Workplace culture empowering role-based flexibility to maximize personal productivity and balance personal needs.
Competitive yearly vacation allowance.
Paid time off for new parents (birthing and non-birthing, including adoptive and foster parents).
Paid time off to care for family members who are sick or injured.
Paid time off to support volunteering and Employee Resource Group’s (ERG) participation.
Wellbeing Portal for all Dow employees, our one-stop shop to promote wellbeing, empowering employees to take ownership of their entire wellbeing journey.
On-site fitness facilities to help stay healthy and active (availability varies depending on location).
Employee discounts for online shopping, cinema tickets, gym memberships and more.
Transportation allowance (availability varies depending on location)
Meal subsidiaries/vouchers (availability varies depending on location)
Carbon-neutral transportation incentives e.g. bike to work (availability varies depending on location)