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Data Platform Engineering Manager Jobs in Michigan

HARMAN International

Data Platform Lead

Novi, MI · On-site

... and engineering investments. * Partner with the Data Platform Portfolio Manager on demand ... dependencies, sequencing, and capacity while retaining ownership of the platform capability and ...

FastTek

Platform Engineer

Dearborn, MI · On-site

Data Governance and Security: Implement and manage data governance, access controls, and security ... data engineering. * Automation and Reliability: Automate data platform processes to enhance ...

Domino's Pizza

Data Platform Engineer

Ann Arbor, MI · On-site

Collaborate with Data Engineering, DataOps, Infrastructure, Security, DBA, and vendor teams to resolve production issues and support platform operations. * Participate in incident management, change ...

Domino's

Data Platform Engineer

Ann Arbor, MI · On-site

Collaborate with Data Engineering, DataOps, Infrastructure, Security, DBA, and vendor teams to resolve production issues and support platform operations. * Participate in incident management, change ...

Domino's Corporate

Data Platform Engineer

Ann Arbor, MI · On-site

Collaborate with Data Engineering, DataOps, Infrastructure, Security, DBA, and vendor teams to resolve production issues and support platform operations. * Participate in incident management, change ...

General Motors

Data Engineering Manager

Warren, MI

$107K - $129K/yr

About the Role The Data Engineering Manager will lead a team of data engineers focused on building ... If you're passionate about distributed systems, Databricks, AI, cloud platforms, and building ...

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Data Platform Engineering Manager information

What does a data platform engineering manager do?

A Data Platform Engineering Manager leads teams responsible for designing, building, and maintaining the core infrastructure and tools that support data storage, processing, and analysis. They oversee the development of scalable and reliable data platforms, ensuring data is accessible, secure, and performant for various business needs. This role also involves collaborating with data engineers, analysts, and other stakeholders to align platform capabilities with organizational goals, as well as mentoring team members and managing project delivery.

What are the key skills and qualifications needed to thrive as a data platform engineering manager?

To thrive as a Data Platform Engineering Manager, you need deep expertise in data architecture, engineering best practices, and leadership, typically supported by a degree in computer science or a related field. Experience with big data technologies (such as Hadoop, Spark, or cloud data platforms), database systems, and relevant certifications like AWS Certified Data Analytics are commonly required. Strong communication, problem-solving, and team management skills distinguish top performers in this role. These competencies are vital for building robust data infrastructure, leading effective teams, and ensuring data solutions align with business objectives.

What are some common challenges faced by data platform engineering managers when scaling data infrastructure, and how can they be addressed?

Data Platform Engineering Managers often encounter challenges such as ensuring data reliability, maintaining performance as data volume grows, and managing cross-functional stakeholder expectations. Addressing these challenges involves implementing robust monitoring and alerting, adopting scalable architectures like distributed data systems, and fostering clear communication between engineering, analytics, and business teams. Staying informed about industry best practices and encouraging continuous learning within the team can also help overcome scaling obstacles while supporting innovation.

What is the difference between Data Platform Engineering Manager vs Data Engineer?

AspectData Platform Engineering ManagerData Engineer
ResponsibilitiesOversees data platform architecture, manages teams, ensures platform scalability and reliabilityBuilds, tests, and maintains data pipelines and infrastructure
Required SkillsLeadership, data architecture, cloud platforms, team managementSQL, ETL, programming, data modeling
CertificationsCloud certifications, data management certificationsNone typically required, but data-related certifications are common
Work EnvironmentManagement, strategic planning, cross-team collaborationHands-on data pipeline development, coding, troubleshooting

The Data Platform Engineering Manager focuses on leading data platform teams and strategic architecture, while Data Engineers are primarily responsible for building and maintaining data pipelines. Both roles require technical skills, but the manager role emphasizes leadership and oversight.

Infographic showing various Data Platform Engineering Manager job openings in Michigan as of August 2026, with employment types broken down into 85% Full Time, 12% Part Time, 2% Contract, and 1% Nights. Highlights an 84% Physical, 3% Hybrid, and 13% Remote job distribution.

Data Platform Lead

Novi, MI • On-site

HARMAN International
Manufacturing • 10K+ employees
Learn more about HARMAN Internationalexternal

Full-time

Re-posted 21 hours ago

Job description

General information
Location:
Novi - Michigan, USA - Cabot Drive
Job Family:
Artificial Intelligence & Machine Learning
Worker Type Reference:
Regular - Permanent
Pay Rate Type:
Salary
Career Level:
M3
Job ID:
R-53799-2026
Description & Requirements
Introduction: A Career at HARMAN Automotive
We're a global, multi-disciplinary team that's putting the innovative power of technology to work and transforming tomorrow. At HARMAN Automotive, we give you the keys to fast-track your career.
  • Engineer audio systems and integrated technology platforms that augment the driving experience
  • Combine ingenuity, in-depth research, and a spirit of collaboration with design and engineering excellence
  • Advance in-vehicle infotainment, safety, efficiency, and enjoyment

About the Role
We are looking for a Data Platform Lead to own the technical direction, architecture, engineering capability, operation, and continuous evolution of HARMAN Automotive's Enterprise Data Platform.
Built primarily on Microsoft Azure and Azure Databricks, the platform enables enterprise data engineering, governed data products, data sharing, analytics, and AI at scale. The Data Platform Lead is accountable for the technical architecture and engineering standards of the platform and for ensuring it is secure, reliable, scalable, cost-effective, and easy for domain teams to adopt.
The role leads a multidisciplinary technical team spanning Platform Engineering, Data Engineering, Data Product Enablement, and Site Reliability Engineering. The Platform Lead sets technical direction, develops engineering talent, balances platform engineering and reliability priorities, and ensures the team can support data product teams without becoming the permanent development organization for every business domain.
The Data Platform Lead works closely with the Data Platform Portfolio Manager, Enterprise Architecture, Cybersecurity, Analytics, MDM and Data Quality, business domains, and other technology teams. Enterprise Architecture provides enterprise standards, architecture review, and alignment, while the Data Platform Lead retains accountability for the architecture and technical evolution of the Enterprise Data Platform.
An external implementation partner will initially build significant portions of the platform. The Data Platform Lead will work alongside the partner, retain HARMAN's technical decision rights, ensure continuous knowledge transfer, and establish the internal capabilities required to operate and evolve the platform as external implementation capacity decreases.
What You Will Do
  • Own the technical architecture, engineering direction, and evolution of the Enterprise Data Platform, with Azure Databricks as the strategic enterprise data and AI platform.
  • Define and maintain platform architecture standards, technical patterns, architecture decisions, and guardrails across Databricks, Azure services, identity, networking, security, storage, governance, automation, and operations.
  • Own the technical platform roadmap and lifecycle, including platform capabilities, architecture evolution, technical debt, reliability improvements, automation, and engineering investments.
  • Partner with the Data Platform Portfolio Manager on demand, dependencies, sequencing, and capacity while retaining ownership of the platform capability and technical roadmap.
  • Manage the platform as an enterprise product and service with defined capabilities, service expectations, measurable outcomes, adoption goals, and a strong focus on developer and user experience.
  • Lead a multidisciplinary technical team spanning Platform Engineering, Data Engineering, Data Product Enablement, and Site Reliability Engineering.
  • Set clear technical direction and accountability across the team and develop senior technical talent capable of owning major platform capabilities and engineering domains.
  • Balance engineering capacity across platform development, reliability, technical debt, automation, operational improvement, reusable capabilities, and targeted data product enablement.
  • Lead the architecture and implementation of Azure Databricks capabilities including workspaces, compute, jobs, Delta Lake, Unity Catalog, data sharing, platform security, and enterprise deployment patterns.
  • Define Azure integration patterns for identity, networking, private connectivity, storage, secrets, security controls, monitoring, logging, and shared enterprise services.
  • Own the technical governance architecture for the platform, including Unity Catalog standards, permissions, access patterns, lineage, ownership, data sharing, and policy enforcement in partnership with Cybersecurity and enterprise governance teams.
  • Establish scalable platform engineering practices including Infrastructure as Code, Terraform, CI/CD, automated testing, environment management, configuration management, deployment automation, and reusable engineering components.
  • Lead the development of API-driven and automated self-service platform capabilities that allow domain teams to onboard, provision environments, request access, deploy workloads, and publish governed data products efficiently.
  • Establish golden paths, reusable platform services, engineering standards, and reference implementations that allow data product teams to deliver consistently without unnecessary central dependency.
  • Establish and continuously improve the platform operating model, including observability, reliability objectives, incident and problem management, resilience, recovery, performance, capacity, and operational automation.
  • Ensure the platform provides appropriate monitoring, service health visibility, cost transparency, usage metrics, and operational reporting.
  • Apply a strong cost and value mindset across Azure and Databricks consumption, platform services, licensing, capacity, and engineering investments.
  • Establish FinOps practices including consumption visibility, cost allocation, workload optimization, capacity forecasting, and showback or chargeback where appropriate.
  • Lead the hub-and-spoke technical operating model by establishing enterprise platform standards and shared capabilities while enabling domain teams to retain ownership of their business logic and data products.
  • Work with less mature domain teams to accelerate onboarding and adoption through targeted enablement, reusable patterns, and technical support.
  • Work with mature spokes and domain platforms as design partners, identifying proven capabilities and engineering patterns that can be reused, federated, or adopted rather than unnecessarily rebuilt within the central platform.
  • Drive technical interoperability across domains, including common standards for data products, sharing, identity, governance, deployment, observability, and cross-domain integration.
  • Partner with Enterprise Architecture to ensure alignment with enterprise technology principles and architecture governance while retaining technical architecture accountability for the Enterprise Data Platform.
  • Partner with Analytics to ensure strong integration with Power BI and enterprise analytics capabilities while maintaining clear ownership boundaries between the platform and analytics functions.
  • Partner with MDM, Data Quality, and Governance teams to provide the technical capabilities required for governance, quality, metadata, stewardship, lineage, security, and compliance without assuming ownership of those enterprise functions.
  • Serve as HARMAN's technical counterpart to implementation partners and systems integrators, retaining architecture and engineering decision rights and reviewing major designs, deliverables, and technical decisions.
  • Ensure external partners transfer platform knowledge, source code, Terraform, automation, documentation, runbooks, architecture decisions, operational procedures, and engineering capabilities to the internal HARMAN team.
  • Build the internal technical capability required for HARMAN to operate, support, and evolve the platform as external implementation capacity ramps down.
  • Manage platform technology vendors and service providers, including technical performance, delivery quality, service levels, dependencies, and commercial considerations.
  • Drive platform adoption by engaging with data product teams and domain leaders, identifying friction, measuring usage and service health, and continuously improving platform capabilities.
  • Support the rationalization and retirement of redundant or legacy data platform technologies as workloads transition to the strategic enterprise platform.

What You Need to Be Successful
  • 8+ years of experience in enterprise data platforms, data engineering platforms, cloud platforms, or related technology environments, with demonstrated technical leadership responsibility.
  • Experience leading technical teams responsible for platform engineering, data engineering, reliability, or enterprise data services.
  • Strong experience with Microsoft Azure and Azure Databricks in enterprise production environments.
  • Strong understanding of Azure Databricks architecture, including workspaces, compute, jobs and workflows, Delta Lake, Unity Catalog, data sharing, platform security, and enterprise deployment patterns.
  • Strong understanding of Microsoft Azure architecture, including identity, networking, private connectivity, storage, secrets management, security, monitoring, and integration with enterprise services.
  • Demonstrated experience owning architecture decisions, technical standards, and platform evolution for a production enterprise platform.
  • Experience with Unity Catalog, including catalog architecture, permissions, identity and access patterns, technical governance, lineage, ownership, and data sharing.
  • Strong experience with Infrastructure as Code and platform automation using technologies such as Terraform.
  • Experience with DevOps and DataOps practices, including CI/CD, Azure DevOps or GitHub, automated testing, automated provisioning, deployment automation, environment management, and configuration management.
  • Experience designing or operating self-service platform capabilities for engineering or data teams.
  • Experience establishing engineering standards, golden paths, reusable services, templates, and reference architectures.
  • Strong understanding of platform reliability and operations, including observability, monitoring, incident and problem management, resilience, disaster recovery, performance, capacity, and service-level expectations.
  • Understanding of cloud economics and FinOps principles, including consumption monitoring, cost allocation, workload optimization, showback or chargeback, and capacity forecasting.
  • Experience working within federated, hub-and-spoke, or similarly distributed data and technology operating models.
  • Experience enabling domain engineering teams within enterprise standards and guardrails without unnecessarily centralizing delivery.
  • Experience leading and developing senior engineers and building distributed technical ownership within a team.
  • Experience managing systems integrators, implementation partners, or major technology vendors, including technical oversight, architecture review, knowledge transfer, and transition to internal ownership.
  • Strong stakeholder management skills with the ability to work effectively across engineering, architecture, cybersecurity, analytics, AI, infrastructure, governance, business domains, procurement, and senior leadership.
  • Ability to make clear technical and investment trade-offs across architecture, engineering complexity, reliability, security, cost, delivery speed, technical debt, and business value.
  • Strong communication and decision-making skills with the ability to communicate technical direction, architecture decisions, risks, and trade-offs to both technical teams and senior business leaders.

Bonus Points if You Have
  • Previous experience as a Data Platform Lead, Data Platform Engineering Manager, Data Engineering Manager, Lead Data Architect, Cloud Platform Lead, or similar enterprise technical leadership role.
  • Deep experience operating Azure Databricks as a strategic enterprise data platform.
  • Experience with advanced Unity Catalog capabilities, Delta Sharing, workspace architecture, compute policies, serverless capabilities, and enterprise-scale governance patterns.
  • Experience with Databricks Asset Bundles, Databricks repositories, deployment patterns, and CI/CD integration.
  • Experience with Spark architecture, workload optimization, cluster and compute management, and large-scale data engineering patterns.
  • Familiarity with MLflow, model lifecycle management, feature engineering, or other Databricks AI and ML platform capabilities.
  • Experience implementing API-driven self-service provisioning for workspaces, environments, access, infrastructure, and platform services.
  • Experience establishing SRE practices including SLOs, SLIs, observability, recovery automation, reliability engineering, and operational readiness.
  • Experience building or operating platform capabilities that support governed enterprise data products.
  • Experience working with mature domain engineering organizations as design partners in a federated enterprise model.
  • Experience with enterprise data sharing and cross-domain interoperability patterns.
  • Experience leading the transition from externally implemented technology platforms to internally owned engineering and operations teams.
  • Experience supporting the rationalization or migration of legacy data warehouses, data platforms, ETL technologies, or analytics environments.
  • Knowledge of software engineering best practices applied to data platform engineering and data product delivery.
  • Relevant certifications in Microsoft Azure, Databricks, cloud architecture, data engineering, or related technical disciplines.

What Makes You Eligible
  • Ability to work from an office

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