IT Manager - Data Platform & Engineering

Improving America’s infrastructure isn’t for the weak. It takes grit, determination, and hard work to execute high impact projects. Michels Corporation engages 10,000 people and 18,000 pieces of heavy equipment in our insatiable drive to be the best. Our work improves lives. Find out how a career as an IT Manager of Data Platform & Engineering can change yours.

The IT Manager of Data Platform & Engineering leads the development and evolution of Michels’ enterprise data platform. As a founding leader on the Enterprise Intelligence team, they build and lead the data engineering function, establish engineering best practices, and deliver trusted, governed data products. This role partners across business and IT teams to enable analytics, reporting, operational insights, and AI while creating a scalable data foundation for the future.

Why Michels?

• We are consistently ranked among the top 10% of Engineering News-Record’s Top 400 Contractors
• Our steady, strategic growth revolves around a commitment to quality
• We are family owned and operated
• We invest an average of $5,000 per employee on training each year
• We reward hard work and dedication with limitless opportunities
• We believe it is everyone’s responsibility to promote safety, regardless of job titles.
• We offer a comprehensive benefits program including (depending on your positions and location you may participate in a different benefit plan):
  • Health, Dental, Life, Flexible Spending Accounts, Health Savings Account, Short Term and Long-Term Disability Insurance, 401(k) plan, Legal Plan, and Identity Theft and Monitoring Plan  

Why you?

• You thrive in fast-paced environments under tight deadlines
• You enjoy collaborating and communicating with your teammates
• You like to know your efforts are noticed and appreciated
• You have strong time management, verbal, and written communication skills

Key Responsibilities:

• Recruit, hire, and develop the founding data engineering team, establishing the roles, standards, and operating practices needed to scale responsibly.
• Lead the buildout of the enterprise data platform on the selected technology stack, implementing the ingestion, storage, transformation, and orchestration layers that convert raw source data into trusted, reusable assets.
• Partner with business and technology stakeholders to understand their needs and sequence the engineering backlog, making deliberate tradeoffs across value, speed, cost, risk, and maintainability.
• Stay hands-on as a technical leader, working alongside the team to design, build, and optimize the pipelines and transformation logic behind priority data products.
• Collaborate with data architecture and data governance peers to ensure platform decisions are durable, interoperable, and ready to support future analytics and AI use cases.
• Establish the foundational engineering disciplines that enable a lean team to reliably operate and support what it builds, using AI and automation to extend capacity.

Qualifications:

• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related quantitative or technical field, or equivalent hands-on experience.
• 8+ years in data engineering, data platform engineering, or software engineering, including 4+ years leading technical teams with accountability for hiring, coaching, and delivery.
• Proven experience building and operating large-scale, complex cloud data platforms (e.g., Databricks, Snowflake, Microsoft Fabric, BigQuery) and delivering governed, reusable data products serving whole organizations across departments and technical personas.
• Deep hands-on proficiency with SQL, Python, and modern data engineering practices including orchestration, transformation, automated testing, CI/CD and both batch and streaming ingestion.
• Experience building reliable, observable pipelines with monitoring, lineage, and data quality controls that operate within enterprise governance, privacy, and security requirements.
• Track record of building AI-ready data platforms that are well-documented, well-structured, and trustworthy enough to serve both analytics and AI/GenAI use cases.

AA/EOE/M/W/Vet/Disability

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