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Data Engineering Jobs in Valparaiso, IN (NOW HIRING)

DwyerOmega

Director, Data Engineering

Michigan City, IN · On-site

We are seeking a visionary Director, Data Engineering to architect the "data set of the future." This role is not just about reporting; it is about building the scalable, AI-ready infrastructure that ...

DwyerOmega

** Engineering Technician

Michigan City, IN · On-site

$30 - $35/hr

Engineering Technician ABOUT THE COMPANY: DwyerOmega is a globally trusted leader in manufacturing ... Collect, analyze, and document test data and prepare technical reports. * Support root cause ...

Amazon

Data Center Chief Engineer, DCEO

Hobart, IN · On-site

Join our dynamic Data Center Engineering Operations Team and become a critical architect of the infrastructure that powers global cloud computing. You'll play a pivotal role in maintaining the ...

Amazon

Data Center Chief Engineer, DCEO

Hobart, IN · On-site

Join our dynamic Data Center Engineering Operations Team and become a critical architect of the infrastructure that powers global cloud computing. You'll play a pivotal role in maintaining the ...

Google

Data Center Technician III

Michigan City, IN · On-site

... Engineer, Help Desk Technician, etc.). * Experience working within a data center or network ... operation center environment. * Experience with Linux operating systems. * Experience in project ...

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All JobsData Engineering JobsData Engineering Jobs in Valparaiso, IN

Data Engineering information

See Valparaiso, IN salary details

$45.9K

$164.8K

$243.2K

How much do data engineering jobs pay per year?

As of Sep 2, 2026, the average yearly pay for data engineering in Valparaiso, IN is $164,811.00, according to ZipRecruiter salary data. Most workers in this role earn between $133,300.00 and $169,800.00 per year, depending on experience, location, and employer.

What is data engineering?

A Data Engineering job involves designing, building, and maintaining the infrastructure that enables efficient data collection, storage, and processing. Data Engineers develop pipelines to transform raw data into usable formats for analytics and machine learning. They work with databases, big data technologies, and cloud platforms to ensure data is accessible and reliable. Their role is crucial for organizations to make data-driven decisions and optimize business processes.

What does a data engineer do?

Data Engineers regularly design, build, and maintain scalable data pipelines to support analytics and business intelligence teams. Their daily tasks often involve working with large datasets, optimizing data storage, ensuring data integrity, and troubleshooting data-related issues. Collaboration with data scientists, analysts, and software engineers is common to align on data requirements and improve workflows. You may also participate in regular code reviews and contribute to the ongoing improvement of data infrastructure. This role is ideal for problem-solvers who enjoy working with both code and complex systems in a collaborative, fast-paced environment.

What skills and qualifications are needed to thrive as a data engineer?

To thrive in Data Engineering, you need a solid background in programming (such as Python, Java, or Scala), data modeling, and database management, typically supported by a degree in computer science or a related field. Familiarity with ETL tools, cloud platforms like AWS or Azure, big data frameworks (e.g., Hadoop, Spark), and relevant certifications is highly valued. Strong problem-solving abilities, effective communication, and the ability to work collaboratively across teams are key soft skills for this role. These attributes are crucial for designing robust data pipelines, ensuring data quality, and enabling organizations to make data-driven decisions efficiently.

Are data engineers still in demand?

Data engineers are currently in high demand due to the increasing reliance on data-driven decision making and the growth of big data technologies. They typically need skills in SQL, cloud platforms, and tools like Apache Spark or Hadoop, and job opportunities are expected to remain strong as organizations continue to prioritize data infrastructure.

What are the most commonly searched types of Data Engineering jobs in Valparaiso, IN?

The most popular types of Data Engineering jobs in Valparaiso, IN are:

What are popular job titles related to Data Engineering jobs in Valparaiso, IN?

For Data Engineering jobs in Valparaiso, IN, the most frequently searched job titles are:

What cities near Valparaiso, IN are hiring for Data Engineering jobs?

Cities near Valparaiso, IN with the most Data Engineering job openings:

Data Engineering jobs near you
Infographic showing various Data Engineering job openings in Valparaiso, IN as of August 2026, with employment types broken down into 88% Full Time, 6% Part Time, and 6% Nights. Highlights an 94% In-person, and 6% Hybrid job distribution, with an average salary of $164,811 per year, or $79.2 per hour.

Director, Data Engineering

Dwyer Instruments, Inc.

Michigan City, IN • On-site

$165K/yr

Full-time

Posted 16 days ago

Job description

Job Type
Full-time
Description
We are seeking a visionary Director, Data Engineering to architect the "data set of the future." This role is not just about reporting; it is about building the scalable, AI-ready infrastructure that will fuel our next generation of manufacturing innovation. You will move the organization beyond traditional data warehousing to a robust Data Lakehouse architecture, ensuring our enterprise data-from shop floor to point-of-sale-is clean, real-time, and ready for advanced GenAI and predictive modeling.
The ideal candidate is a technologist who fluently bridges the gap between the plant floor and the front office. You will be responsible for integrating complex operational data with high-velocity sales and commercial data to create a unified ecosystem. By connecting factory efficiency directly to customer demand and market trends, you will enable us to pivot from reactive operations to a truly predictive enterprise.
Key Responsibilities:
  • Architecting the Future: Define and execute a data infrastructure roadmap centered on a Lakehouse architecture that integrates structured and unstructured data, enabling both real-time operational analytics and high-scale AI/ML workloads.
  • AI-Ready Foundation: Establish the data governance, cataloging, and lineage frameworks necessary to power secure, trusted AI models and Large Language Models (LLMs) across the enterprise.
  • Manufacturing Integration: Partner with OT and Engineering teams to ingest and operationalize IIoT and supply chain data, creating a unified data ecosystem that drives predictive maintenance and factory floor efficiency.
  • Modern Data Stack Leadership: Oversee the transition from legacy BI tools to modern, self-service analytics platforms, ensuring the organization has the agility to derive insights from the data lakehouse.
  • Data Ops & Governance: Lead the transition to MLOps and DataOps methodologies, ensuring data quality, security, and compliance in an increasingly automated environment.
  • Strategic Partnership: Collaborate with business unit leaders to identify and prioritize data products that drive measurable top-line growth or operational cost reductions.
  • Team Leadership: Build and mentor a high-performing team of data engineers, ML engineers, and data architects who are comfortable in both cloud-native environments and complex legacy manufacturing systems.

Requirements
Qualifications and Technical Requirements:
  • Strategic Experience: 15+ years in data strategy, architecture, and engineering, with at least 5 years in a leadership role driving organizational change.
  • 5+ years in a leadership role managing data & analytics teams.
  • Architecture Expertise: Demonstrated experience designing and deploying Lakehouse architectures (e.g., Databricks, Snowflake, or similar) at scale.
  • AI/ML Fluency: Proven experience operationalizing AI/ML models within an enterprise environment; deep understanding of data preparation for LLMs and generative AI.
  • Cloud Proficiency: Extensive experience with Azure (or equivalent cloud hyperscaler) data stacks (e.g., Synapse/Fabric, ADLS Gen2, Azure AI).
  • Tooling: Advanced proficiency in Python, Spark, and SQL; strong experience with CI/CD for data pipelines and infrastructure-as-code.
  • Education: Bachelor's or Master's degree in Computer Science, Data Engineering, or a related technical field.
  • Soft Skills: A "product manager" mindset for data; the ability to translate complex technical architectural debt into business-friendly value proposition

Essential/Preferred Skills:
  • Experience with data governance frameworks and tools.
  • Exposure to advanced analytics, data science, or machine learning initiatives.
  • Experience in manufacturing, industrial, or eCommerce environments preferred.

Work Conditions and Physical Requirements:
  • Ability to work in both office and manufacturing environments.
  • Availability to work outside of core business hours, including nights, weekends, and holidays when required for system upgrades or migrations.
  • Required to sit or stand for long periods of time.
  • The ability to lift 30-50 lbs without assistance.
  • Local and/or international travel will be required as needed (10-15%) including some extended stays on location for education or deployments. Must have a valid driver's license and Passport.

Salary Description
165,000

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