Data Engineer

THE POSITION Our roster has an opening with your name on it We are looking for a Data Engineer to join our growing data platform team and take end-to-end ownership of designing, building, and scaling the foundational data infrastructure that powers analytics, machine learning, and business decision-making across the company. In this role, you will independently drive the design and delivery of reliable, secure, and cost-efficient data platforms, enabling data engineers, analysts, and data scientists to do their best work. You will lead technical efforts in close partnership with engineering, analytics, and security teams — improving platform reliability, performance, and developer experience for high-impact data workloads at scale. You will bring strong engineering judgment to ambiguous technical problems, drive architectural decisions, and actively contribute to growing the technical capability of the team. The ideal candidate is an experienced engineer who takes ownership of complex systems end-to-end, solves ambiguous problems independently, and thrives in a fast-paced, collaborative environment. If you’re excited by building resilient data platforms and want to make a broad impact at a dynamic company, we’d love to hear from you. In addition to the specific responsibilities outlined below, employees may be required to perform other such duties as assigned by the Company. This ensures operational flexibility and allows the Company to meet evolving business needs. THE GAME PLAN Everyone on our team has a part to play Own the design, development, and delivery of scalable data pipelines and platform components built on Databricks and Airflow — from requirements through production — with clear accountability for reliability, performance, and maintainability. Drive performance monitoring and optimization of data workflows: proactively identify bottlenecks, diagnose root causes, and implement improvements independently. Lead design and code reviews, setting the bar for code quality, testability, and engineering standards across the team’s data engineering work. Make independent technical decisions on pipeline architecture, data modeling, and tooling trade-offs, grounded in scalability, cost efficiency, and operational simplicity. Own operational excellence for production batch and near-real-time pipelines: build and maintain monitoring, alerting, and runbooks, and lead incident response and postmortems. Build and maintain comprehensive documentation for pipelines, data models, and platform workflows that supports team knowledge-sharing and onboarding. Partner with analytics, data science, and engineering teams to drive adoption of platform capabilities, enforce data governance standards, and deliver against shared roadmap commitments. Mentor junior engineers through pairing, code review feedback, and technical guidance — actively contributing to the growth of engineering craft on the team. THE STATS What we're looking for in our next teammate Minimum Qualifications 3+ years of experience with Apache Airflow or a comparable orchestration platform, including building and maintaining DAGs for production workloads, with strong command of retries, scheduling, dependencies, and sensor patterns. 3+ years of experience developing in Python, writing readable, testable, and maintainable code in a data engineering context. 3+ years of experience with Databricks or a comparable distributed data platform, including designing and delivering ETL/ELT pipelines using Delta Lake or similar technologies, and independently optimizing compute and query performance. Demonstrated ability to design and own end-to-end data systems with a focus on scalability, reliability, and operational observability — not just pipeline execution, but the full delivery lifecycle. Solid experience working in cloud environments (AWS, Azure, or GCP), including cloud storage, IAM, and managed services, with strong understanding of secure data access patterns. Experience wit