Sr Data Engineer

Applicants must be authorized to work in the U.S.;
Sponsorship is not available for this position.

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 support to your business unit. 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.

As a Senior Data Engineer, you will deliver innovative data products that enable Milwaukee Tool to make fast, data-driven decisions. Working closely with business partners and the Data Platform team, you will design, build, and support the core systems that power our data platform. You will create scalable data pipelines, self-service tools, and governance solutions that ensure trusted, accessible data across the organization.

You’ll develop deep knowledge of our data and support advanced analytics, including machine learning. Success in this role requires curiosity, a passion for experimentation, and a drive to share knowledge to elevate the team and benefit our customers.

• Design and build scalable data pipelines to ingest, transform, and curate data from a variety of systems including APIs, databases, files, and event streams.
• Lead technical design reviews, translate complex business needs into enterprise-grade data solutions, and influence data engineering best practices across teams.
• Develop and optimize advanced data models (dimensional, data vault, domain-driven, canonical models) to support analytics, BI, and productized datasets.
• Champion engineering excellence through SDLC best practices, continuous delivery, and data infrastructure automation using CI/CD and Infrastructure-as-Code.
• Optimize complex distributed workloads including SQL, Python, and Spark; mentor others on advanced tuning techniques and scalable design patterns.
• Build reusable data frameworks, libraries, and reference architectures to accelerate team productivity and platform adoption.
• Perform root‑cause analysis for major data incidents, lead long-term remediation, and influence operational reliability improvements.
• Provide technical mentorship to Data Engineers, guide code reviews, and help shape engineering capability maturity.
• Collaborate with Architects, Data Leads, Product Owners, and cross‑functional engineering teams to define long‑term data strategies.
• Performs other duties as assigned.

• Bachelor’s degree in Computer Science, Information Systems or equivalent experience.
• 5 to 8+ years of experience in data engineering or a related technical field.
• Expertise in SQL and advanced proficiency in at least one programming language (Python preferred).
• Extensive hands‑on experience building scalable pipelines and workflows in Databricks (Delta Lake, Spark, Unity Catalog, Jobs, Workflows).
• Hands‑on experience with distributed data processing technologies such as Apache Spark.
• Strong experience designing and tuning distributed data processing systems at scale.
• Proven experience designing and implementing complex data models across multiple business domains.
• Strong knowledge of version control, CI/CD, Dev Ops/Data Ops, automated testing, and engineering best practices.
• Ability to lead cross‑functional engineering initiatives and influence technical roadmaps.
• Strong problem‑solving, debugging, and analytical skills, especially in complex, multi‑system environments.
• Ability to thrive in agile, dynamic, and collaborative engineering teams.

• Experience with Databricks Unity Catalog, Delta Live Tables, or Databricks Workflows.
• Data Ops experience (pipeline observability, monitoring, automated quality).
• Knowledge of metadata management or cataloging platforms (Purview, Collibra, Alation).
• Experience with streaming frameworks (Kafka, Event Hubs, Kinesis) used with Spark Structured Streaming.
• Knowledge and experience working in…