Data Warehouse Architect 3

TekWissen • Lansing, MI

22h • Hybrid

About The Position

TekWissen is recruiting for a temporary 11-month assignment with a state government client in Lansing, Michigan. The role is described as a Data Engineering Lead focused on data modeling, Lakehouse and Medallion Architecture, and delivery of data and machine-learning products using agile DevOps practices and distributed data architectures. The position serves as the primary client technology lead, working with the UoM proof-of-concept team to support best-practice adoption in the state hosting environment and provide training to DAV data engineers.

The role oversees data pipeline design, development, and maintenance; architects and optimizes data infrastructure, storage, orchestration, and ETL/ELT processes; and establishes practices for data quality, reliability, security, governance, and validation. It also provides technical leadership and mentoring to data engineering staff and collaborates with data scientists, analysts, product teams, and business stakeholders. The work involves cloud data platforms and technologies including Spark, Kafka, Kubernetes, SQL, NoSQL and graph databases, Databricks, SageMaker, DBT, Docker, and Power BI or Tableau.

Requirements

• Bachelor’s degree; the posting specifies a BS in Computer Science, Data Science, or Information Systems.
• At least 4 years of hands-on experience hydrating an enterprise data lake on AWS, Azure, or Google Cloud from relational, semi-structured, and unstructured data sources by building extract-and-load pipelines.
• At least 4 years of hands-on experience transforming datasets in a cloud data lake into data products and tracing their attributes or features to critical data elements in systems of record.
• At least 2 years of experience enforcing data-sharing policies so users see only the data product version to which they are entitled when subscribing.
• At least 4 years of experience designing and implementing data, analytics, and AI solutions for large enterprises using a cloud service provider’s data platform tools.
• At least 4 years of experience modeling and leveraging relational data services, with proficiency in Python, PySpark, and SQL, including tuning SQL queries to extract data from multiple sources.
• At least 4 years of experience with programming or scripting languages including Python, R, and Spark.
• Experience designing and delivering a star-schema-based operational data store for visualizing program activities and outcomes in a tool such as Power BI.
• Knowledge or demonstrated proficiency in data modeling, Lakehouse and Medallion Architecture, distributed data architectures, and data/ML product delivery.
• Technologies and languages listed include SQL, Python, R, Spark, Terraform, DAGs, YAML, Docker, Juniper Notebooks, NoSQL and graph databases, Parquet, Kafka, Kubernetes, Glue, Databricks, Power BI, Tableau, Redshift Spectrum, SageMaker, DBT, DataHub, GitHub, and DevOps.

Nice To Haves

• Proficiency with DevOps tools such as GitHub, Jenkins, Terraform, YAML, DAGs, Docker, Databricks, and SageMaker for building data/ML pipelines that extract and store data from multiple sources in Parquet destinations.
• Experience promoting data engineering standards and best practices to encourage scalable, supportable data/ML product delivery and use.
• Experience implementing cloud service provider data governance and security tools to automate enforcement of data-sharing policies when subscribers access data products.

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.
• Establish standards and monitoring systems and implement validation and governance practices to ensure data quality, reliability, and security.
• Lead and mentor data engineering staff by providing technical guidance, reviewing designs and code, and supporting professional growth.
• Collaborate with data scientists, analysts, product teams, and business stakeholders to ensure data solutions meet organizational needs and strategic goals.
• Serve as the primary client technology lead, engaging with the UoM proof-of-concept team to support best-practice adoption in the state hosting environment.
• Provide training to DAV data engineers.
• Promote data engineering standards and best practices among developers.
• Design and deliver data pipelines and data products using cloud service provider platforms and distributed data architectures.

What This Job Offers

• Career Level: Senior