Staff Data Engineer-Promo Analytics

Milwaukee Tool•Menomonee Falls, MT

About The Position

Staff Data Engineer-Promo Analytics Applicants must be authorized to work in the U.S.; Sponsorship is not available for this position now or in the future. 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 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. Your Role on Our Team: As a Staff Data Engineer , you will be a senior technical leader who helps drive, architect, and deliver innovative data products that enable Milwaukee Tool to move with Speed, Agility, and Urgency. You will design, develop, and support foundational systems, particularly focused on retail promotions, of our enterprise data platform. This includes developing scalable data processing pipelines, creating self‑service analytical capabilities, implementing governance frameworks, and enabling advanced analytics such as machine learning. You will also mentor other engineers, guide best practices, and shape long‑term data engineering strategy. A successful candidate will have a deep passion for learning, experimentation, leadership, and elevating engineering excellence across the organization.

Requirements

Bachelor’s degree in Computer Science, Information Systems or equivalent experience.
8 or more years of experience in data engineering or a related technical field.
Expert‑level proficiency in SQL, Python, Spark, and large‑scale data processing.
Extensive experience designing and building complex data pipelines, data models, and distributed data systems (Delta Lake, Spark, Unity Catalog, Jobs, Workflows).
Strong experience designing and tuning distributed data processing systems at scale.
Proven experience designing and implementing complex data models across multiple business domains.
Demonstrated mastery of data engineering best practices including version control, CI/CD, automated testing, DevOps/DataOps, and observability.
Proven ability to lead cross‑functional technical initiatives and influence architectural direction.
Strong problem‑solving, debugging, and analytical skills, especially in complex, multi-system environments.
Ability to thrive in agile, dynamic, and collaborative engineering teams.

Nice To Haves

Experience with Databricks Unity Catalog, Delta Live Tables, or Databricks Workflows.
Skilled in advanced data modeling (dimensional, data vault, semantic layers).
DataOps experience (pipeline observability, monitoring, automated quality).
Experience with metadata management and governance platforms (Unity Catalog, Purview, Collibra, Alation).
Experience with streaming frameworks (Kafka, Event Hubs, Kinesis) used with Spark Structured Streaming.
Strong communication, interpersonal, and leadership skills.
Knowledge and experience working in an Agile environment.
Experience working with retail product promotion data.

Responsibilities

Partner with business stakeholders and engineering leadership to understand cross‑domain analytical needs and translate them into scalable architectural designs.
Lead and architect large‑scale data pipelines and transformation frameworks that support enterprise‑wide analytics and advanced data products.
Define, champion, and implement data engineering standards, best practices, and patterns across the Data Platform organization.
Design and optimize complex distributed data processing systems using technologies such as Spark, Databricks, and cloud‑native data services.
Develop canonical data models, semantic structures, and reusable datasets that support enterprise reporting and machine learning initiatives.
Drive platform modernization initiatives (e.g., Delta Lake, streaming architectures, metadata‑driven design).
Provide technical leadership, guidance, and mentorship to other data engineers, enabling rapid, high‑quality delivery.
Lead root‑cause analysis for major data issues and drive long‑term, systematic improvements in data quality, lineage, and observability.
Influence engineering roadmaps, architectural decisions, and platform strategy across multiple teams and business units.
Other duties and responsibilities as assigned.