
1001 - 5000 employees
Founded 2017
âď¸ SaaS
⥠Productivity
đ˘ Enterprise
đ° $400M Series C - ClickUp on 2021-10
SaaS ⢠Productivity ⢠Enterprise
ClickUp is a cloud-based SaaS work management and productivity platform that consolidates projects, docs, chat, time tracking, automations and AI-driven "Super Agents" into a single workspace. It helps teams and enterprises streamline workflows, replace fragmented software stacks, and boost productivity with integrations, customizable agents/workflows, and enterprise-grade security and compliance.
đĽ 5 minutes ago
đşđ¸ United States â Remote
đľ $139k - $181.5k / year
â° Full Time
đ Senior
đ° Data Engineer
đŚ H1B Visa Sponsor
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1001 - 5000 employees
Founded 2017
âď¸ SaaS
⥠Productivity
đ˘ Enterprise
đ° $400M Series C - ClickUp on 2021-10
SaaS ⢠Productivity ⢠Enterprise
ClickUp is a cloud-based SaaS work management and productivity platform that consolidates projects, docs, chat, time tracking, automations and AI-driven "Super Agents" into a single workspace. It helps teams and enterprises streamline workflows, replace fragmented software stacks, and boost productivity with integrations, customizable agents/workflows, and enterprise-grade security and compliance.
⢠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.
⢠Equity ⢠401k ⢠Health, Dental, and Vision insurance ⢠Spending accounts ⢠Life & Disability ⢠Paid parental leave ⢠Flexible paid time off ⢠Enhanced employee assistance program ⢠Employee wellness stipend ⢠Professional development stipend
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