Key skills
PythonAIMLGenerative AILLMData EngineeringSnowflakeBigQuerySparkKafkaAirflowKubernetesLeadershipMentoringCommunication
About this role
Chartbeat Inc. is the parent company of Chartbeat, Tubular Labs, FatTail, and Lineup Systems, focusing on shaping the future of media strategy and revenue. The Staff Engineer role in Data Engineering involves designing and contributing to cross-division data architecture while driving the AI roadmap and collaborating with various teams to enhance data products and insights.
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 prototype emerging AI and generative AI technologies for application to real-world product challenges
- Participate in engineering planning and contribute to roadmap prioritization with a cross-division perspective
- Be part of engineering on call rotation to monitor and maintain the health of our production systems
Requirements:
- 8+ years of experience in data engineering, data architecture, or a closely related discipline
- Proven experience designing large-scale data systems across complex, multi-product or multi-division environments
- Strong proficiency with cloud data warehouse technologies (Snowflake, BigQuery, or equivalent)
- Experience building and maintaining data pipelines at scale, streaming and batch, using tools such as Kafka, Spark, Airflow, or similar
- Demonstrated experience integrating AI and ML capabilities into data systems, including LLMs or foundation models in production environments
- Strong Python and Data Architecture experience
- Excellent communication skills - able to translate complex architectural decisions for both technical and non-technical audiences
- A track record of elevating engineering teams through mentorship, documentation, and technical leadership
- Experience with Kubernetes or containerized infrastructure is a plus