$ cat jobs/staff-engineer-data-engineering-chartbeat-inc-efbb55c8a8af.json
Staff Engineer - Data Engineering
Chartbeat Inc. is the parent company of Chartbeat, Tubular Labs, FatTail, and Lineup Systems. Together, we’re shaping the future of media strategy and revenue. Trusted by the world's top media brands, Chartbeat, Inc. combines analytics that power smarter audience strategies with revenue solutions that simplify ad operations and accelerate monetization. Our mission is to help customers grow valuable media brands with their content. Join our diverse group of focused, hardworking professionals who are passionate about doing work that’s challenging and fun — and who strive to maintain a healthy work/life balance. About the Role This Staff Engineer role sits within Data Engineering and spans both the Chartbeat and Tubular divisions. The team includes several data engineers embedded across the two divisions. You will be a senior technical voice and contributor across all of them elevating architecture, enabling cross-division collaboration, and driving the AI roadmap. You will contribute and influence architectural decisions that determine whether our platforms compound or diverge as we integrate, and you will stay close enough to the code and contribute to it. Responsibilities Design, own and contribute to the cross-division data architecture, establishing patterns and standards that scale across both Chartbeat and Tubular platforms Lead the technical strategy for integrating data products across divisions, enabling new cross-brand features and insights Drive the AI and backend infrastructure roadmap - from data pipelines that feed models to deployment patterns for LLM-powered features Identify and close skill gaps across the data engineering team, actively mentoring engineers and elevating the collective technical ceiling Collaborate with product, engineering, and ML teams to translate business needs into scalable architectural decisions Establish and evolve best practices for data modeling, pipeline design, and system observability across the organization Evaluate and
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