Staff Data Engineer

About the role

At ClickUp we’re building the future of work with an AI‑native workspace. As a Staff Data Engineer you will own the architecture and technical vision of our data platform, set the technical bar for the team, drive cross‑functional alignment, and solve our hardest engineering problems.

Key responsibilities

• Own the technical architecture of ClickUp’s data platform, balancing scalability, cost, reliability, and velocity.
• Define and drive the technical roadmap for data infrastructure in partnership with leadership.
• Design systems at scale, building frameworks, abstractions, and patterns used daily by engineers.
• Lead complex, cross‑team initiatives spanning data engineering, analytics, data science, and data analytics.
• Drive cost optimization across cloud infrastructure and compute.
• Build and evolve data pipelines using AWS serverless services, Snowflake, and dbt.
• Establish engineering standards for observability, testing, CI/CD, code review, and documentation.
• Design and maintain infrastructure for AI/ML workloads, including LLM frameworks and model monitoring.
• Mentor senior engineers and raise overall engineering quality.
• Influence org‑wide technical decisions and represent data engineering in architecture discussions.

Required profile

• Significant professional experience in data or backend/infrastructure engineering, with at least 3 years at a senior or staff level.
• Proven track record of owning architecture for large‑scale data platforms or distributed systems.
• Deep expertise in AWS services (Lambda, Fargate, Step Functions, S3, Kinesis, DynamoDB, Aurora) and infrastructure‑as‑code (Terraform or CDK).
• Expert‑level SQL and Snowflake knowledge, including performance tuning and cost optimization.
• Strong experience with dbt and modern ELT/ETL patterns at scale.
• Advanced Python skills for building reusable libraries and tooling.
• Hands‑on experience with orchestration frameworks (Airflow, Dagster, or Prefect) in production.
• Experience building data infrastructure for AI/ML, such as feature stores and training pipelines.
• Deep understanding of streaming and event‑driven architectures (Kinesis, Kafka).
• Mastery of CI/CD, Git workflows, containerization (Docker), and deployment automation.
• Strong communication skills and a track record of mentoring engineers.

Required skills

• AWS (Lambda, Fargate, Step Functions, S3, Kinesis, DynamoDB, Aurora)
• Terraform (or CDK)
• Snowflake
• SQL
• dbt
• Python
• Airflow, Dagster, or Prefect
• CI/CD pipelines
• Git
• Docker
• Kinesis, Kafka