Staff Data Engineer

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About This Role

ClickUp operates as an AI-native organization developing an AI workspace that combines tools like tasks, documentation, chat, calendars, and enterprise search. The company expects all staff members to use AI daily and evaluates applicants for AI fluency. ClickUp seeks a Staff Data Engineer to guide the architecture and technical strategy of its data platform. This remote, full-time position offers a yearly salary between USD 169,000 and 211,000. This autonomous role…

Job Description

Responsibilities

• Manage the technical architecture of the data platform by balancing velocity, cost, reliability, and scalability
• Partner with leadership to define and execute the technical roadmap for data infrastructure
• Design large-scale systems by creating patterns, abstractions, and frameworks utilized daily by other engineers
• Lead complex technical initiatives across data science, analytics, data engineering, and data analytics teams
• Optimize cloud compute and infrastructure costs to drive efficiency
• Develop and improve data pipelines utilizing Snowflake, dbt, and AWS serverless components such as Lambda, Silla, Fargate, Step Functions, Kinesis, DynamoDB, and Aurora
• Implement and promote engineering standards regarding testing, CI/CD, documentation, observability, and code reviews
• Build and support infrastructure for machine learning and artificial intelligence workloads, including LLM frameworks, feature pipelines, training data systems, and model monitoring
• Provide technical guidance, conduct design reviews, and mentor senior engineers
• Represent the data engineering team in company-wide architecture discussions and guide organizational technical choices

Requirements

• Minimum of three years of professional experience operating at a senior or staff level in backend, infrastructure, or data engineering
• Demonstrated history of owning architecture for large-scale distributed systems or data platforms
• Deep proficiency in AWS cloud services like S3, Lambda, Kinesis, DynamoDB, Aurora, and Fargate alongside infrastructure as code tools such as Terraform or CDK
• Expertise in Snowflake and advanced SQL, encompassing cost optimization and performance tuning
• Extensive background working with dbt and modern ELT or ETL patterns at scale
• Advanced Python proficiency focused on creating reusable tooling, frameworks, and libraries
• Practical production experience with orchestration frameworks including Dagster, Prefect, or Airflow
• Background in building data infrastructure for AI and machine learning, such as model serving, feature stores, training pipelines, embedding pipelines, or LLM integration
• Solid understanding of event-driven and streaming architectures like Kafka, Kinesis, or equivalents
• Mastery of containerization with Docker, Git workflows, deployment automation, and CI/CD
• Strong communication abilities to write technical RFCs, influence without authority, and explain trade-offs to non-technical stakeholders
• Proven background in growing and mentoring engineers

Qualifications

• Prior experience running data platforms at high scale, such as petabyte-scale warehouses or millions of events per second
• Familiarity with data product or data mesh paradigms
• Background in FinOps practices and cloud cost management at scale
• Previous experience in a technical leadership position without direct reports on the staff or principal individual contributor track
• Contributions to technical communities or open-source data tools

Core Skills

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