Data Warehouse Architect 3 Data Engineering Lead

Position: Data Warehouse Architect 3 / Data Engineering Lead
Location: Lansing, Michigan
Work Arrangement: Hybrid Wednesday and Thursday onsite (mandatory)
Education: Bachelor's Degree Required
Interview Process: First Round Virtual; Second Round In-Person

• Wednesday and Thursday are mandatory onsite days.

• Remote-only candidates are not accepted.

• Candidates local to the Lansing, Michigan area are preferred.

• Non-local candidates may apply if they are willing to relocate at their own expense before the assignment start date.

• Relocating candidates must be available to work onsite from Day 1.

• The hybrid schedule is non-negotiable.

The Data Engineering Lead / Data Warehouse Architect will lead the design, development, and delivery of enterprise data engineering, analytics, and machine learning solutions.

The ideal candidate will have strong expertise in data modeling, Lakehouse and Medallion Architecture, distributed data systems, and cloud-based data platforms. The role requires hands-on experience building data pipelines, transforming enterprise data into reusable data products, and enforcing data-sharing policies.

The selected candidate will serve as the primary technology lead, working with a proof-of-concept team to promote best practices within the enterprise hosting environment and provide technical training to data engineers.

• Oversee the design, development, and maintenance of data pipelines for moving, transforming, and preparing data for analytics and operational systems.

• Architect and optimize data infrastructure, including storage solutions, orchestration frameworks, and ETL/ELT processes.

• Design and implement scalable Lakehouse and Medallion Architecture solutions.

• Ensure data quality, reliability, and security through standards, monitoring, validation, and governance practices.

• Lead and mentor data engineering staff by providing technical guidance and reviewing architecture, designs, and code.

• Collaborate with data scientists, analysts, product teams, and business stakeholders to deliver solutions aligned with organizational goals.

• Work with proof-of-concept teams to support best-practice adoption within the enterprise hosting environment.

• Provide training and technical guidance to data engineers.

• Promote enterprise data engineering standards and scalable Data/ML product delivery practices.

• Ensure traceability of data product attributes and features to Critical Data Elements in systems of record.

• Implement data-sharing policies to ensure subscribers can access only the versions of data products they are authorized to use.

• Support the development of operational data stores and visualization solutions for program activities and outcomes.

• Bachelor's degree in Computer Science, Data Science, Information Systems, or a related field.

• Minimum 4 years of hands-on experience building and loading enterprise data lakes on cloud service providers such as AWS, Azure, or Google Cloud.

• Experience building Extract and Load data pipelines using relational, semi-structured, and unstructured data sources.

• Minimum 4 years of experience transforming cloud data lake datasets into reusable data products.

• Experience maintaining traceability between data product attributes/features and Critical Data Elements in systems of record.

• Minimum 2 years of experience implementing and enforcing data-sharing policies for data products.

• Minimum 4 years of experience designing and implementing enterprise data, analytics, and AI solutions using cloud data platforms.

• Minimum 4 years of experience with relational data modeling and Python, PySpark, and SQL programming.

• Strong experience tuning SQL queries and extracting data from multiple sources, including relational databases, flat files, CSV, HTML, and JSON.

• Minimum 4 years of experience with programming and scripting languages such as Python, R, and Spark.

• Experience designing and delivering Star Schema-based operational data stores.

• Experience building data solutions for visualization using Power BI or similar tools.

• Experience with DevOps tools such as GitHub, Jenkins, Terraform, YAML, DAGs, Docker, Databricks, and SageMaker.

• Experience building Data/ML pipelines to extract and store data from multiple sources into Parquet destinations.

• Ability to establish and promote data engineering standards and best practices across development teams.

• Experience implementing data governance and security tools available through cloud service providers.

• Experience automating data-sharing policy enforcement when subscribers access data products.

• Experience with scalable and supportable Data/ML product delivery for analytics and machine learning users.

Programming Languages and Scripting

• SQL

• Python

• R

• PySpark

• Apache Spark

• Terraform Scripting

• DAG Scripting

• YAML

• Docker

Cloud & Data Engineering Platforms

• AWS

• Microsoft Azure

• Google Cloud Platform

• Databricks

• AWS Glue

• Amazon SageMaker

• Amazon Redshift Spectrum

• DBT

Data Architecture & Technologies

• Lakehouse Architecture

• Medallion Architecture

• Star Schema

• Data Modeling

• ETL/ELT Pipelines

• Parquet

• Relational Databases

• NoSQL Databases

• Graph Databases

• Kafka

• Kubernetes

Analytics, Governance & DevOps

• Power BI

• Tableau

• Juniper Notebooks

• DataHub

• Machine Learning

• GitHub

• Jenkins

• Terraform

• Data Governance

• Data Security

• Data Sharing Policies

• Data Engineering Leadership

• Data Warehouse Architecture

• Cloud Data Lake Development

• Lakehouse and Medallion Architecture

• Data Modeling and Star Schema

• ETL/ELT Pipeline Development

• Python, PySpark, and SQL

• Data Product Development

• Data Governance and Data Sharing Policies

• AWS / Azure / Google Cloud

• Apache Spark and Kafka

• Kubernetes and Docker

• Databricks and SageMaker

• DBT and AWS Glue

• Terraform, Jenkins, and GitHub

• NoSQL and Graph Databases

• Parquet Data Formats

• Power BI and Tableau

• Machine Learning and AI Solutions

• DevOps and Data Engineering Best Practices