Sr Manager IT- Data Engineering

Milwaukee Tool•Menomonee Falls, MT

About The Position

Applicants must be authorized to work in the U.S.; Sponsorship is not available for this position. INNOVATE without boundaries! At Milwaukee Tool we firmly believe that our People and our Culture are the secrets to our success -- so we give you unlimited access to everything you need to provide technical solutions on our IT Team. Behind our doors you'll be empowered every day to own it, drive it, and do what it takes to support the biggest breakthroughs in the industry. Meanwhile, you'll have the support and resources of the fastest-growing brand in the construction industry to make it happen. Your Role on Our Team: The Senior Manager of Data Engineering leads teams that design, build, operate, and continuously improve enterprise data products and platforms. Reporting to the Director of Data Engineering, this leader is accountable for reliable delivery, engineering quality, team development, and strong partnerships across business, analytics, architecture, governance, security, and technology teams. This role will advance our Databricks-based data platform and establish practical AI-native engineering capabilities. The Senior Manager will use AI-assisted and agentic development approaches to improve how teams design, code, test, document, deploy, monitor, and support data solutions while maintaining appropriate human oversight, security, data governance, and software engineering controls.

Requirements

Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent relevant experience.
7 or more years of progressive experience in data engineering, software engineering, data platforms, or related disciplines, including at least three years leading engineering teams and people managers.
Demonstrated experience leading enterprise-scale data engineering delivery in a complex, cross-functional environment.
Hands-on technical depth with Databricks and Apache Spark, including Delta Lake and production pipeline design; Unity Catalog and Databricks Workflows experience is strongly desired.
Strong experience with Python, SQL, automated testing, source control, CI/CD, infrastructure as code, and cloud-native delivery.
Experience designing and operating batch and streaming pipelines, lakehouse or warehouse solutions, APIs, and event-driven integrations.
Experience implementing or governing AI-assisted development tools and practices in an enterprise engineering environment.
Working knowledge of data and AI governance, security, privacy, metadata, lineage, data quality, and production controls.
Strong business acumen, written and verbal communication, problem solving, prioritization, and executive stakeholder management skills.

Nice To Haves

Experience with Microsoft Azure, including Azure Data Lake Storage, Azure DevOps, Entra ID, Event Hubs, or related services.
Experience modernizing enterprise data warehouses and ETL estates, including migration, coexistence, and decommissioning.
Experience in manufacturing, supply chain, retail, product, or other data-intensive domains.
Experience managing global teams, strategic partners, and managed services.

Responsibilities

Lead, coach, and develop data engineering managers, technical leads, and engineers; establish clear accountability, performance expectations, career paths, and succession plans.
Build an inclusive, high-performing engineering culture grounded in ownership, technical excellence, collaboration, and continuous learning.
Translate enterprise strategy into priorities, capacity plans, delivery roadmaps, and measurable outcomes.
Manage employee and partner capacity, budgets, and vendor relationships to deliver the highest-value work efficiently.
Communicate effectively with executives, business leaders, architects, product leaders, and technical teams.
Own the delivery and operation of scalable data pipelines, reusable frameworks, curated data products, and platform capabilities supporting analytics, reporting, operational, AI, and machine learning use cases.
Partner with product and business leaders to define outcomes, prioritize demand, manage dependencies, and deliver incremental business value.
Establish engineering standards for architecture, coding, testing, CI/CD, observability, documentation, performance, reliability, and supportability.
Improve delivery predictability through portfolio management, agile planning, transparent metrics, risk management, and disciplined execution.
Reduce technical debt and operational burden through modernization, automation, reusable patterns, and intentional lifecycle management.
Lead incident response and problem management for critical data services, including root-cause analysis and corrective action.
Provide technical and operational leadership for the Databricks Lakehouse platform, including Delta Lake, Unity Catalog, workflows, compute, security, observability, and cost management.
Guide migration and modernization of legacy data pipelines and warehouse workloads to scalable Databricks patterns where they provide clear value.
Establish reusable ingestion, transformation, orchestration, data quality, deployment, and monitoring frameworks.
Optimize platform performance and cost through workload design, cluster and serverless strategies, usage transparency, and FinOps practices.
Define and scale responsible AI-assisted development practices across requirements, design, code generation, review, testing, documentation, deployment, and operations.
Evaluate agentic engineering capabilities that automate bounded workflows while preserving human approval, traceability, security, and production controls.
Set measurable adoption and effectiveness goals for AI development tools, including cycle time, quality, test coverage, developer experience, and incident reduction.
Ensure AI-generated code and artifacts meet enterprise standards for security, privacy, maintainability, intellectual property, testing, and peer review.
Partner with AI and data science teams to provide trusted data products, feature pipelines, vector and unstructured data patterns, and model or agent telemetry.
Embed data ownership, metadata, lineage, classification, access controls, retention, and quality rules into engineering workflows and platform services.
Define service-level objectives and metrics for data freshness, quality, availability, performance, cost, and recovery.
Partner with cybersecurity, privacy, risk, and compliance teams to ensure data products and AI-enabled workflows meet enterprise requirements.
Performs other duties as assigned.