Data Warehouse Architect

Join our dynamic team in Lansing, MI, where innovation meets opportunity. We are seeking a skilled professional to lead our data engineering efforts and drive the development of cutting-edge data solutions. This role offers the chance to work in a collaborative environment, where your expertise will shape the future of our data infrastructure and analytics capabilities.

Top Skills & Years of Experience

• Clear and concise written and verbal communication is crucial to success and productivity in this role.

Required Education

• Bachelor's Degree

Required Skills

• Minimum 4 years of hands-on experience in hydrating the enterprise Data Lake on a Cloud Service Provider (AWS, Azure, or Google) from relational, semi-structured, and unstructured data sources by building Extract and Load Data Pipelines.
• Minimum of 4 years hands-on experience in transforming datasets in a Data Lake on a Cloud Service Provider to deliver Data Products with traceability of their attributes/features to the Critical Data Elements in the systems of record.
• Minimum of 2 years experience in enforcing Data Sharing Policies to Data Products to ensure that users get to see only the version of the Data Product they are entitled to access at the time of subscription.

Preferred Skills

• Proficiency in DevOps tools including Github, Jenkins, Terraform, YAML, DAGs, Docker, Databricks, SageMaker for building Data/ML pipelines to extract/store data from multiple sources to parquet destinations.
• Experience in promoting Data Engineering Standards and Best Practices among the community of developers to influence the enterprise towards building scalable and supportable Data/ML Products delivery and their usage by the community of data, analytics, and ML product users.
• Experience in implementing Data Governance and Data Security tools available on a CSP to automate the enforcement of Data Sharing Policies at the point of subscription of a Data Product by a subscriber.

Roles and Responsibilities

• Oversee the design, development, and maintenance of data pipelines that move, transform, and prepare data for analytics and operational systems.
• Architect and optimize data infrastructure, including storage solutions, orchestration frameworks, and ETL/ELT processes.
• Ensure data quality, reliability, and security by establishing standards, monitoring systems, and implementing validation and governance practices.
• Lead and mentor data engineering staff, providing technical guidance, reviewing designs/code, and supporting professional growth.
• Collaborate with cross-functional partners, such as data scientists, analysts, product teams, and business stakeholders, to ensure data solutions meet organizational needs and strategic goals.

Other Desired Skills

• BS in Computer Science / Data Science / Information Systems with an overall 4 years of experience in designing and implementing data, analytics, and AI solutions for large enterprises by building data pipelines using the available data platform tools of any Cloud Service Provider (CSP) such as AWS, Microsoft, Google.
• Experience of minimum 4 years in modeling and leveraging Relational data services with demonstrated proficiency in Python/Pysparks/SQL programming and tuning SQL queries to extract data from multiple sources (relational DB, flat files, CSV, HTML, JSON, etc.) sources into Lakehouse Medallion architecture in parquet formats.
• Demonstrated experience and expertise of minimum 4 years in programming/scripting languages including Python, R, Spark, etc.
• Experience in designing and delivering Star-schema based operational data store is required to enable visualization of program activities and outcomes in a visualization tool such as Power BI.

Key Technologies

• Programming Languages: SQL, Python, R, Spark, Terraform Scripting, DAGs Scripting, YAML scripting, Docker
• Other Technologies, Concepts, and Frameworks: Juniper Notebooks, NOSQL DB, Graph DB, Parquet DB, Lakehouse and Medallion Architecture concepts, Kafka, Kubernetes, Glue, Databricks, Power BI, Tableau, Redshift Spectrum, SageMaker, DBT, Datahub, Machine Learning, GitHub, DevOps