Senior Data Engineer

Responsibilities

• 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.
• 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.

Requirements

• 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.

Nice-to-Haves

• Experience operating data platforms at high scale (petabyte-level warehouses, millions of events/sec).
• Familiarity with data mesh or data product paradigms.
• Experience with FinOps practices and cloud cost management at scale.
• Prior experience in a technical leadership role without direct reports (staff/principal IC track).
• Contributions to open-source data tools or technical communities.