We are looking for a hands-on Lead Data Engineer / Cloud Data Warehouse Architect to design, build, and optimize scalable cloud data pipelines, Lakehouse architectures, and enterprise data/ML products. In this position, you will establish data engineering best practices, enforce data governance policies, mentor engineering staff, and collaborate across data science and analytics teams.
Role: Lead Data Engineer / Cloud Data Warehouse Architect
Location: Lansing, MI (Onsite required from Day 1; occasional travel to Ann Arbor, MI)
Work Arrangement: Hybrid (Mandatory in-office on Wednesdays & Thursdays — no remote-only option)
Education: Bachelor’s Degree in Computer Science, Data Science, or Information Systems
About the Position
We are looking for a hands-on Lead Data Engineer / Cloud Data Warehouse Architect to design, build, and optimize scalable cloud data pipelines, Lakehouse architectures, and enterprise data/ML products. In this position, you will establish data engineering best practices, enforce data governance policies, mentor engineering staff, and collaborate across data science and analytics teams.
Must-Have Skills & Experience
- Cloud Data Lake Engineering (4+ Years): Hands-on experience ingesting relational, semi-structured, and unstructured data into enterprise Data Lakes (AWS, Azure, or Google Cloud Platform) via Extract & Load pipelines.
- Data Transformation & Lineage (4+ Years): Transforming cloud Data Lake datasets into production Data Products with clear traceability to Critical Data Elements (CDEs) in source systems.
- Data Modeling & Programming (4+ Years): Strong proficiency in SQL, Python, PySpark, and R, query tuning, Star-schema operational data stores, and Lakehouse / Medallion Architecture (Parquet formats).
- Data Governance & Access Control (2+ Years): Enforcing row/column-level Data Sharing Policies so subscribers only access entitled data versions.
- Communication: Clear, concise written and verbal communication to lead technical discussions and mentor peers.
Preferred Tech Stack
- Cloud & Big Data: Databricks, Spark, Kafka, AWS Glue, Redshift Spectrum, SageMaker, DBT
- DevOps & Orchestration: Terraform, Docker, Kubernetes, GitHub, Jenkins, YAML, DAGs
- Databases & BI: Relational, NoSQL, Graph DBs, Power BI, Tableau
Interview Process
- Round 1: Virtual interview (Candidates must join from a laptop, be ready to share their screen, and provide a live verification photo).
- Round 2: In-person interview in Lansing, MI.
