Own the technical architecture of ClickUp's data platform, making design decisions that balance scalability, cost, reliability, and velocity.
Define and drive the technical roadmap for data infrastructure in partnership with leadership.
Design systems at scale: build frameworks, abstractions, and patterns that other engineers use daily.
Lead complex, cross-team technical initiatives spanning data engineering, analytics engineering, data science, and data analytics.
Drive cost optimization across cloud infrastructure and compute, turning efficiency into a competitive advantage.
Build and evolve our data pipelines using AWS serverless (Lambda, Fargate, Step Functions, Kinesis, S3, DynamoDB, Aurora), Snowflake, and dbt.
Establish and champion engineering standards: observability, testing, CI/CD, code review, and documentation practices.
Design and maintain infrastructure for AI/ML workloads, including LLM frameworks, feature pipelines, training data systems, and model monitoring.
Mentor senior engineers, provide technical guidance through design reviews, and raise the overall engineering quality of the team.
Influence org-wide technical decisions and represent data engineering in company-level architecture discussions.

Significant professional experience in data engineering or backend/infrastructure engineering, with at least 3 years operating at a senior or staff level.
Proven track record of owning architecture for data platforms or large-scale distributed systems.
Deep expertise in AWS cloud services (Lambda, Fargate, Step Functions, S3, Kinesis, DynamoDB, Aurora) and infrastructure as code (Terraform and/or CDK).
Expert-level SQL and Snowflake (or equivalent cloud data warehouse) knowledge, including performance tuning and cost optimization.
Strong experience with dbt and modern ELT/ETL patterns at scale.
Advanced Python skills with emphasis on building reusable libraries, frameworks, and tooling.
Hands-on experience with orchestration frameworks (Airflow, Dagster, or Prefect) in production environments.
Experience building data infrastructure for AI/ML: feature stores, training pipelines, embedding pipelines, model serving, or LLM integration.
Deep understanding of streaming and event-driven architectures (Kinesis, Kafka, or equivalent).
Mastery of CI/CD, Git workflows, containerization (Docker), and deployment automation.
Strong communication skills: ability to write technical RFCs, influence without authority, and translate complex trade-offs for non-technical stakeholders.
Track record of mentoring and growing engineers, with a multiplier mindset.

Senior Data Engineer

Key skills