The Senior Data Engineer will design and build data pipelines and systems, optimize data workflows, and mentor engineers. Responsibilities include developing data models and ensuring reliable data operations across teams.
102469 - Sr. Data EngineerSummary
As a Senior Data Engineer, you will design, build, and support the core systems that power Milwaukee Tool’s data platform, enabling fast, data-driven decisions across the organization. This role focuses on delivering scalable data pipelines, self-service tools, and governance solutions that ensure trusted, accessible data for analytics and machine learning.
You will work closely with business partners and the Data Platform team to create enterprise-grade data products, influence engineering best practices, and mentor other engineers. The role offers the opportunity to shape long-term data strategy, improve operational reliability, and accelerate adoption of a modern data platform built on Azure and Databricks technologies.
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 distributed workloads using SQL, Python, and Spark; mentor others on tuning and scalable design patterns.
- Build reusable data frameworks, libraries, and reference architectures to accelerate team productivity.
- Perform root-cause analysis for major data incidents and lead long-term remediation to improve operational reliability.
- Provide technical mentorship, guide code reviews, and help shape engineering capability maturity.
- Collaborate with Architects, Data Leads, Product Owners, and cross-functional teams to define long-term data strategies.
- Perform other duties as assigned.
- Bachelor’s degree in Computer Science, Information Systems, or equivalent experience.
- 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).
- Hands-on experience with Azure.
- 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.
- 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.
- Strong problem-solving, debugging, and analytical skills in complex, multi-system environments.
- Ability to lead cross-functional engineering initiatives and thrive in agile, collaborative teams.
- Experience with Databricks Unity Catalog, Delta Live Tables, or Databricks Workflows.
- 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.
- Experience working in an Agile environment.
Top Skills
Spark
Azure
Ci/Cd
Databricks
Dataops
DevOps
Python
SQL
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