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.
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.
