Data Engineer Senior

At Zebra, we are a community of innovators who come together to create new ways of working. United by curiosity and a culture of caring, we develop smart solutions that anticipate our customer’s and partner’s needs and solve their challenges. Being part of Zebra Nation means you are seen, heard, valued, and respected. Drawing from our unique perspectives, we collaborate to deliver on our purpose. Here you are part of a team pushing boundaries today to redefine the work of tomorrow for organizations, their employees, and those they serve.   You’ll have opportunities to learn and lead in a forward-thinking environment, defining your path to a fulfilling career while channeling your skills toward causes you care about—locally and globally. Come make an impact every day at Zebra.

We are seeking a Senior Data Engineer to lead the technical direction of our data engineering practice while remaining deeply hands-on. This is a role for someone who has built and owned production data pipelines end-to-end, thrives on solving complex data architecture problems, and brings the leadership presence to guide and grow a team of engineers. We're looking for someone with a strong technical foundation, a positive and collaborative attitude, and a genuine curiosity to learn and adopt new technologies as the data landscape evolves — including staying current with the latest capabilities on both Databricks and Google BigQuery..

Lead the technical direction of the data engineering team — setting standards, guiding design decisions, and reviewing the team's work for quality and scalability Design, build, and maintain new, scalable data pipelines to onboard and integrate data from a wide variety of source systems Work extensively within the Databricks platform, applying strong command of the medallion architecture (bronze, silver, gold layers) to build and evolve reliable, well-structured pipelines Stay current with new Databricks capabilities (e.g., Unity Catalog, Delta Lake features, Delta Live Tables, Databricks SQL, workflow/orchestration updates) and proactively apply them to improve pipeline design, governance, and performance Design and build new data models grounded in solid data fundamentals, including fact and dimension modeling Own and optimize workloads in Google BigQuery, staying current with new BigQuery features (e.g., BQML, materialized views, storage/compute optimizations, new SQL capabilities) and applying them to improve performance, cost efficiency, and scalability What You'll Bring 7–10 years of experience in data engineering, with a demonstrated track record of building and owning production-grade pipelines end-to-end Proven experience leading a team on technical matters — architecture decisions, code reviews, and mentorship Strong, hands-on experience with the Databricks platform, including practical, working knowledge of the medallion (bronze/silver/gold) architecture and demonstrated awareness of new/evolving Databricks features Strong, hands-on experience with Google BigQuery, including demonstrated awareness and adoption of new BigQuery features Hands-on proficiency in Python, SQL, and Git version control Solid grounding in data modeling fundamentals — facts, dimensions, and the ability to design new data models from the ground up Demonstrated ability to build pipelines across diverse and evolving source systems Preferred Qualifications Bachelor's degree in Computer Science, Engineering, or related field (or equivalent practical experience) Cloud certification(s) — Databricks Certified Data Engineer, Google Cloud Professional Data Engineer, or equivalent Experience with orchestration/workflow tools (Airflow, Databricks Workflows, dbt) Experience with CI/CD for data pipelines (Git-based deployment, testing frameworks for data) Exposure to streaming/near-real-time pipelines (Kafka, Pub/Sub, Structured Streaming) Experience with data governance, cataloging,