Senior Data Engineer

Dynamic Lifecycle Innovations•Onalaska, WI

9d•$85,000 - $120,000•Hybrid

About The Position

At Dynamic Lifecycle Innovations, we give electronics their next best life while protecting our planet and our customers’ interests. As a Certified Great Place to Work® since 2017, we’ve built a team culture that’s rewarding, engaging, and FUN! But don’t just take our word for it—see what our Team Members have to say. At Dynamic Lifecycle Innovations, we believe great performance comes from alignment across three areas: Head – your natural behavioral drives and cognitive agility (measured by our Predictive Index assessment), Heart – your values, passions, and what drives you to make a meaningful impact, and Briefcase – your experiences, skills, and results from past roles. Every step of our process is designed to understand you in all three areas. It’s how we ensure the best mutual fit, not just for what you can do, but for how you’ll thrive here long-term. You’ll start with the Predictive Index (PI) assessment (takes <20 minutes total), then move through structured interviews, work samples, and culture-focused interviews. We respect your time and promise clear, timely communication throughout. As our Senior Data Engineer, you’ll serve as the primary technical owner of our Azure Databricks lakehouse and the integrations that keep data moving across our enterprise. You’ll build reliable bronze, silver, and gold data layers, create trusted data products for Power BI, analytics, automation, and AI, and develop secure application integrations using MuleSoft and other technologies. Your work will turn complex data into dependable, reusable assets, helping teams make better decisions while establishing the engineering standards, architecture, and governance that allow our data platform to scale.

Requirements

Bachelor’s degree in computer science, information technology, software engineering, or a related field. Additional relevant experience will be considered in lieu of formal education.
Five or more years of progressive, hands-on experience designing, developing, deploying, and supporting enterprise data pipelines, analytical data models, and application integrations.
Production experience with cloud-based ingestion, transformation, dimensional modeling, data quality, monitoring, troubleshooting, and curated data delivery for reporting or analytics.
Advanced SQL skills and proficiency with Python and PySpark for production-grade pipelines and transformations.
Experience applying Git-based source control, CI/CD, automated deployment, and environment configuration practices to data engineering workloads.
Deep understanding of lakehouse and medallion architecture, including bronze, silver, and gold data layers.
Strong command of batch, streaming, incremental, change-data-capture, API-based, and file-based ingestion patterns.
Ability to design dimensional models and curated data products that support reporting, self-service analytics, machine learning, and AI.
Strong diagnostic thinking—you can trace discrepancies across source systems, transformation logic, integrations, and reporting layers to find the real cause.
Sound judgment in balancing speed with long-term maintainability, scalability, security, governance, reliability, and cost.
Clear communication and documentation skills, with the ability to translate business requirements into technical designs and collaborate across technical and non-technical teams.
Working knowledge of secure cloud-data practices, including access control, secrets management, encryption, service principals, and environment separation.
You’re a hands-on builder who enjoys creating order from complexity and taking meaningful ownership of the platforms behind the scenes.
You are curious enough to explore better approaches, disciplined enough to build for production, and persistent enough to stay with a difficult data issue until it is truly resolved.
You collaborate with humility, communicate clearly, share what you know, and care deeply about earning trust in both the data and the relationships surrounding it.
Most of all, you’re energized by the chance to build something that scales—and to see your work improve decisions across an entire business.

Nice To Haves

Hands-on experience with Azure Databricks, Delta Lake, Unity Catalog, Databricks Workflows, Auto Loader, Lakeflow Declarative Pipelines, or comparable modern data-platform tooling.
Experience with MuleSoft Anypoint Platform or another enterprise integration/iPaaS platform, plus Power BI datasets or semantic models.
Experience integrating ERP, CRM, finance, logistics, WMS, manufacturing, or other operational systems into an enterprise data platform.

Responsibilities

Own and operate the Azure Databricks lakehouse, helping shape its architecture, standards, governance, reliability, and future roadmap.
Design, build, deploy, and support scalable ingestion pipelines from enterprise applications, APIs, relational databases, flat files, SaaS platforms, and external sources.
Develop and maintain bronze, silver, and gold data layers using Delta Lake, SQL, Python, and PySpark, including dimensional models and business-ready datasets.
Optimize Databricks workloads for performance, scalability, maintainability, reliability, and cost.
Design and support APIs, application interfaces, automated workflows, and file-based integrations using MuleSoft and other integration technologies.
Implement data quality controls, reconciliation, lineage, monitoring, logging, alerting, and recovery practices; lead root-cause analysis when issues occur.
Partner with business stakeholders, BI developers, application owners, architects, vendors, and consultants to translate needs into durable technical solutions and documentation.