Lead Data Engineer (Lakehouse / Databricks)

Stafford Gray is looking for a hands-on Lead Data Engineer to architect and build a modern cloud data lakehouse for a large public-sector organization. You’ll be the technical lead for data engineering. You’ll design and build the pipelines that feed the enterprise data lake, turn raw data into governed data products, and coach the in-house data engineers on standards and best practices. You’ll work alongside data scientists, analysts, and an external research team. This role is for someone who still builds every day and also sets direction for the team., * Design, build, and maintain pipelines that ingest relational, semi-structured, and unstructured data into a cloud data lake on AWS, Azure, or GCP.

• Transform lake datasets through a Medallion (bronze/silver/gold) architecture into Parquet-based data products, with every attribute traceable back to its source system.
• Enforce data sharing and access policies so each subscriber sees only the version of a data product they’re entitled to.
• Design star-schema operational data stores that feed Power BI and Tableau reporting.
• Set data quality, monitoring, validation, and governance standards.
• Lead and mentor data engineers through design reviews, code reviews, and hands-on training.
• Partner with data scientists, analysts, product teams, and business stakeholders to deliver data and ML solutions.

Data Lake, Data Science, Technical Leadership, Best Practices, Coaching, Cloud Computing, Software Engineering, Data Analysis, Unstructured Data, GCP (Good Clinical Practices), * Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related field.

• 4+ years of hands-on experience building extract-and-load pipelines into an enterprise data lake on a major cloud provider.
• 4+ years of hands-on experience transforming data lake datasets into data products with traceability to the systems of record.
• 2+ years of experience enforcing data sharing or access policies on data products.
• 4+ years of programming in Python, PySpark, and SQL, including SQL tuning and loading data from databases, flat files, CSV, JSON, and APIs into Parquet.
• Experience designing star-schema data stores for BI reporting.
• Must live within 90 miles of Lansing, MI at the time of submission and be able to work a hybrid onsite schedule.

Preferred

• Databricks, Spark, DBT, Glue, Redshift Spectrum, and SageMaker.
• Kafka, Kubernetes, and Docker.
• DevOps and infrastructure-as-code: GitHub, Jenkins, Terraform, YAML, and Airflow DAGs.
• Cloud-native data governance and security tooling.
• Experience with NoSQL and graph databases, and with ML pipelines.
• A track record of championing data engineering standards across a developer community.