Sr. Data Engineer

# Summary

As a Senior Data Engineer B., you will design, build, and support the core systems that power Milwaukee Tool’s data platform. You will deliver scalable data products and pipelines that enable fast, data-driven decisions, working closely with business partners and the Data Platform team. The role emphasizes hands-on engineering with Databricks and distributed processing, strong Azure data engineering experience, and a focus on governance, reliability, and self-service tooling.

This position offers the opportunity to shape enterprise-grade data solutions, mentor other engineers, optimize large-scale workloads, and support advanced analytics and machine learning initiatives. Success requires curiosity, a passion for experimentation, and a drive to share knowledge to elevate the team and benefit customers.

Responsibilities

• Design and build scalable data pipelines to ingest, transform, and curate data from APIs, databases, files, and event streams.
• Lead technical design reviews and translate complex business needs into enterprise-grade data solutions.
• Develop and optimize advanced data models (dimensional, data vault, domain-driven, canonical) to support analytics and productized datasets.
• Champion SDLC best practices, continuous delivery, and data infrastructure automation using CI/CD and Infrastructure as Code.
• Optimize complex distributed workloads using SQL, Python, and Spark, and mentor others on tuning 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, 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.

Requirements

• 5 to 7+ 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, DevOps/DataOps, automated testing, and engineering best practices.
• Ability to lead cross-functional engineering initiatives and influence technical roadmaps.
• Strong problem-solving, debugging, and analytical skills in complex, multi-system environments.
• Proven experience with Azure cloud architecture for data engineering, including Azure Databricks integration, ADLS Gen2, Azure Data Factory (ADF), and Key Vault.

Nice to Have

• DataOps 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 an Agile environment.