We are looking for a Staff Data Engineer to shape the direction of enterprise data platforms in Brookfield, Wisconsin. In this role, you will guide technical decisions, partner with engineering and leadership teams, and build scalable solutions across hybrid environments. This position focuses on delivering secure, high-performing data capabilities that support both internal operations and client-facing data products while maintaining strong governance and privacy standards. Health care industry experience is strongly desired.
Responsibilities:
- Provide senior technical guidance on data platform architecture, serving as a key advisor to engineering partners and business leaders.
- Design and evolve scalable frameworks for ingesting, transforming, storing, and governing high-value data across warehouse and lakehouse environments.
- Lead delivery of complex data initiatives by aligning project scope, coordinating contributors, and communicating progress, risks, and outcomes to stakeholders.
- Establish and uphold data governance, lineage, auditability, and stewardship practices, including administration of cataloging capabilities that improve visibility and control.
- Develop and enforce data security standards, including least-privilege access models and protections for sensitive and de-identified healthcare information.
- Architect and optimize robust batch, streaming, and event-driven pipelines that improve reliability, resilience, and processing efficiency at enterprise scale.
- Build and enhance cloud-based data solutions using Databricks, Delta Live Tables, Python, Scala, and ETL technologies to support diverse structured and unstructured datasets.
- Create and maintain infrastructure as code with Terraform and related deployment assets to strengthen platform consistency, scalability, and operational efficiency.
- Develop APIs and integration patterns that connect internal platforms and external systems, enabling dependable data exchange with third-party applications and client solutions.
- Monitor platform performance, manage pipeline health, and partner with the business on cost-conscious designs that balance transparency, value, and long-term sustainability.
- Strong experience designing and implementing enterprise data engineering solutions in cloud-based environments.
- Advanced proficiency with Python and hands-on expertise building ETL workflows for large-scale data processing.
- Demonstrated experience with AWS services, including data pipeline technologies and broader cloud data architecture.
- Deep working knowledge of Databricks and Azure Databricks for data transformation, orchestration, and platform development.
- Experience building reliable batch, streaming, and event-driven data pipelines with a focus on performance and resilience.
- Solid understanding of data governance, access controls, security best practices, and privacy requirements for sensitive data.
- Background creating integrations with external systems and APIs, along with experience supporting data products for internal or client use.
- Ability to lead technical initiatives, influence architectural direction, and collaborate effectively across engineering and leadership teams.
