Lead Data Engineer - Google Cloud & Azure Databricks (Kansas City)

Overview\:

We're looking for a senior engineer who combines an architecture mindset with deep hands-on experience — someone who can design scalable systems and personally build and ship them into production. As our lead data engineer, you will own pipelines end-to-end across Google Cloud and Azure Databricks, from whiteboard to running code, while setting the technical direction for our enterprise data products, mentoring the team, and serving as the bridge between business stakeholders and engineering to turn strategic goals into robust, secure data solutions.

Responsibilities\:

• Architect, build, and ship scalable batch and streaming data pipelines into production using BigQuery Dataform, Google Cloud Dataflow, Confluent Kafka, and Pub/Sub.
• Design and build data pipelines on Azure Databricks using Apache Spark, Delta Lake, and Unity Catalog, keeping engineering patterns consistent with our Google Cloud platform.
• Manage the end-to-end data product lifecycle, serving as the technical owner for critical data assets and establishing clear service level objectives.
• Provide technical leadership and mentorship to junior and senior engineers, conducting code reviews and fostering a culture of technical excellence.
• Lead the engineering team across Google Cloud and Azure Databricks environments, setting multi-cloud architecture standards for data movement, interoperability, security, and cost management.
• Partner with product managers, business analysts, and leadership to define data product requirements, align roadmaps, and manage engineering delivery schedules.
• Define, document, and enforce data engineering standards, including CI/CD practices, robust testing frameworks, and comprehensive technical documentation.
• Own the monitoring, cost optimization, and troubleshooting of production pipelines to maintain reliable business operations.
• Evaluate emerging technologies, particularly generative AI integrations and advanced governance tools, to continuously improve data platform capabilities.