Senior Data Engineer

Role overview Build and improve the data platform that enables engineering and analytics teams to develop and deliver data solutions. The work centers on Databricks and spans platform architecture, integrations, automation, reliability, and cost efficiency, with input into technical planning and design.

Responsibilities - Design and maintain scalable, reliable data platform capabilities, primarily on Databricks. - Develop and optimize data pipelines, integrations, automation, and reusable tools for other teams. - Monitor platform performance and resource use; troubleshoot issues and improve reliability and cost efficiency. - Work with security and engineering partners to align solutions with access controls, governance, and compliance needs. - Help identify shared platform needs, prioritize improvements, and contribute to architecture decisions.

Requirements - At least five years in software engineering, data engineering, or a related discipline, including five years of hands-on Databricks platform work. - Experience building and operating cloud-based data platforms, pipelines, integrations, and production services. - Strong Python and SQL skills; experience with Scala, Apache Spark, Delta Lake, or similar data technologies. - Experience with cloud infrastructure, preferably AWS; Azure or Google Cloud experience is also relevant. - Familiarity with infrastructure as code, CI/CD, automated provisioning, data governance, security, and platform reliability. - Ability to collaborate across engineering teams and take ownership of complex technical issues.

Nice to have - Experience with streaming data systems or event-driven architectures, such as Kafka or Confluent.

Benefits and work setup Travel is generally limited to about 12 days per year. Benefits listed include health, dental, and vision coverage; retirement plan matching; flexible paid time off and holidays; leave programs; a lifestyle spending account; tuition reimbursement; and a phone stipend.