Data Engineer (Databricks)

Role overview

This position centers on designing and running the scalable data infrastructure that powers analytics, AI, and digital product platforms. The engineer will shape ETL/ELT pipelines, data lake and warehouse architectures, and large-scale distributed systems that move and refine high-volume datasets reliably. It is a hands-on role that bridges data producers and downstream consumers, keeping quality, governance, and trust front of mind.

Responsibilities

- Build, maintain, and optimize scalable batch and streaming data pipelines handling high-throughput datasets - Design and evolve data warehouse and data lake architectures using established modeling patterns - Operate distributed computing environments that support reliable, large-scale data processing - Apply governance, cataloging, and security practices (such as Unity Catalog and data contracts) to keep assets trustworthy and discoverable - Collaborate with software engineers producing data and the teams consuming it, translating needs in both directions - Drive CI/CD, version control, and workflow orchestration hygiene across data engineering deliverables

Requirements

- Proven track record designing and operating scalable ETL/ELT pipelines and warehouse or lake architectures - Hands-on experience with distributed computing systems and large, high-volume datasets - Practical knowledge of data modeling approaches such as dimensional modeling, star schemas, or data vault - Advanced SQL plus strong relational and non-relational database design and optimization skills - Familiarity with stream and batch processing paradigms, orchestration tooling, and cloud infrastructure patterns - Grounding in data governance, security best practices, and CI/CD/version control principles - Systems-thinking mindset combined with meticulous attention to data quality, accuracy, and reliability - Working experience with Databricks (including Asset Bundles and Unity Catalog) on Azure, along with Data-as-a-Service and data contract concepts

Nice to have

- Comfort using AI tooling to automate workflows, accelerate research, or augment day-to-day data engineering tasks