The Senior Data Engineer builds the trusted data foundation that powers analytics, reporting, operational insight, and AI across the Michels Family of Companies. As a senior individual contributor on the Enterprise Intelligence team, this person builds and operates lakehouse pipelines and reusable data products and helps shape the engineering standards that turn raw source data into valuable, durable enterprise assets. This is a hands-on role that owns complex work from design through production support and helps a lean team scale through patterns, automation, and observability. This role mentors engineers and provides technical leadership within delivery work. This role reports to the IT Manager, Data Platform & Engineering and is a member of the Information Technology organization. Key Responsibilities: • Build and maintain batch, streaming, and event-driven ingestion pipelines that reliably connect enterprise source systems to the lakehouse. • Develop transformation logic and orchestrated workflows that turn raw source data into usable, governed datasets. • Design and optimize data assets that support analytics, operational reporting, downstream applications, and AI-ready consumption. • Instrument pipelines and data products with validation checks, monitoring, and alerting so data and pipeline issues are detected and resolved quickly. • Tune pipeline, storage, and compute workloads for performance, scalability, and cost efficiency. • Establish engineering standards and reference architectures that allow a lean team to scale responsibly. • Lead technical design reviews and mentor engineers through pairing, code review, and technical guidance. Qualifications: • Bachelor's degree in Computer Science, Information Systems, Engineering, or a related technical field, or equivalent hands-on experience. • 5+ years of production experience in data engineering, data platform engineering, analytics engineering, software engineering, or a related discipline. • Proven experience building and operating large-scale cloud data platforms such as Databricks, Snowflake, Microsoft Fabric, or BigQuery. • Proficiency with SQL, Python, Spark or similar distributed processing frameworks, and modern engineering practices including version control, automated testing, CI/CD, and streaming ingestion. • Experience building reliable, observable pipelines with monitoring, lineage, and data quality controls that operate within enterprise governance, privacy, and security requirements.