About this Data Engineering Manager role at Lake Michigan Credit Union
Primary Location: Grand Rapids
Employee Status: Full-Time
Workplace Type: Hybrid
Who we are:
At LMCU, you'll find more than just a job - discover a fulfilling career where your contributions truly matter. Join our talented team at Lake Michigan Credit Union and discover the difference an employer who puts people first can make in your career and life.
About this position:
The Manager, Data Engineering leads the Data Engineering function responsible for designing, building, maintaining, and optimizing enterprise data models, pipelines, and integrations that support business needs and enable self-service analytics across LMCU. This role manages Senior Data Engineer resources, establishes data engineering standards, oversees delivery and operational support, and ensures data solutions are scalable, reliable, secure, and aligned to business priorities.
What you’ll do:
Lead, coach, and develop Senior Data Engineers, including managing workload priorities, sprint commitments, performance feedback, career development, hiring, and day-to-day delivery expectations.
Establish and maintain enterprise standards for data modeling, ETL/ELT development, orchestration, integration patterns, Microsoft Fabric/OneLake architecture, and reusable data products.
Oversee the design, development, testing, deployment, maintenance, and optimization of data pipelines, curated data models, integrations, and data migrations across core banking, digital, operational, and analytics platforms.
Ensure data pipelines and models are reliable, secure, performant, well-documented, and supportable through effective monitoring, data quality controls, lineage, and issue-resolution processes.
Partner with Business Intelligence, Data Governance, Application Development, Infrastructure, vendors, and business stakeholders to translate business needs into scalable technical solutions.
Enable trusted self-service analytics by delivering reliable, accessible, and well-governed data products that support reporting, analytics, and informed decision-making. Adhere to and champion our core values of curious minds, collaborative hearts, and continuous excellence.
What you’ll bring:
8+ years of progressive experience in data engineering, analytics engineering, data architecture, data warehousing, or data platform development, including experience leading technical resources, delivery workstreams, or project teams.
Bachelor’s degree in computer science, information systems, information technology, data management, data analytics, engineering, or a related field; significant relevant experience may be considered in lieu of a degree.
Hands-on experience with Microsoft Fabric, OneLake, Data Factory, notebooks, lakehouse and warehouse workloads, SQL Server, and modern cloud data platforms.
Strong knowledge of ETL/ELT design and orchestration, dimensional modeling, star and snowflake schemas, Kimball/Inmon concepts, and medallion architecture.
Proficiency with SQL and Python, including data pipeline testing, observability, monitoring, and troubleshooting.
Experience with Azure DevOps/Git, CI/CD practices, and modern development and deployment processes.
Knowledge of data quality controls, metadata management, data lineage, governance, and documentation best practices.
Experience integrating core banking, digital banking, and other operational data sources into enterprise data platforms.
Experience working within Agile delivery environments, including ServiceNow or Jira intake, prioritization, and stakeholder communication.
Relevant certifications in Microsoft Fabric, Azure, cloud data platforms, data engineering, Agile/Scrum, leadership, or project management are preferred.
experience.
Ability to work effectively within established priorities, standards, and processes while demonstrating strong execution and follow-through.
Preferred Qualifications:
Experience partnering with or leading Data Science teams in the delivery of predictive analytics, machine learning, or AI-driven solutions.
Familiarity with the data science lifecycle, including model development, model deployment (MLOps), monitoring, and governance.
Experience building platforms, pipelines, and infrastructure that enable Data Scientists to develop, test, and operationalize models at scale.
Knowledge of modern AI, machine learning, and generative AI technologies and their integration into enterprise data platforms.
Demonstrated ability to bridge Data Engineering, Business Intelligence, and Data Science disciplines to deliver business outcomes.
What you’ll get:
All Employees: weekly pay and retirement savings options.
Full-Time Employees: comprehensive health coverage including medical (with prescription), dental, vision, HSA match, paid parental leave, and tuition reimbursement.
To see a full list of our benefit offerings, check out this helpful guide!
Have additional questions about the role? Email the Talent Acquisition Team at: [email protected].
If you lack access to the internet or require an accommodation in the application process, please send your resume via mail to P.O. BOX 2848, Grand Rapids, MI 49501-2848.
