[All remote jobs](https://jobicy.com/jobs.md)[![ClickUp logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/cd5be0ef-221.jpg)](https://jobicy.com/company/clickup.md)[ClickUp](https://jobicy.com/company/clickup.md) # Staff Data Engineer Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/clickup.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md), [Canada](https://jobicy.com/job-region/canada.md)SalaryUSD 169k–211k / yrDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished25 Oct 2026Apply before24 Nov 2026Listing views46Application actions3Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. ClickUp is seeking a Staff Data Engineer to own the technical architecture and long-term strategy for its data platform. The role combines hands-on data infrastructure engineering with staff-level technical leadership across data engineering, analytics, data science, and AI/ML teams. Core work includes scalable AWS serverless pipelines, Snowflake and dbt development, Terraform-based infrastructure, streaming systems, and cloud cost optimization. The engineer will define engineering standards, mentor senior peers, lead complex cross-team initiatives, and influence company-wide architecture decisions. This is a senior individual-contributor position requiring strong systems design, communication, and platform ownership. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis is a staff-level platform role requiring deep expertise across distributed data systems, cloud infrastructure, AI/ML data workloads, and organizational technical leadership. The candidate must make high-impact architectural trade-offs while mentoring engineers and aligning multiple functions. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$190,000US market range (per year)$180k–$240k0$264k AI insightThe employer discloses a yearly base-pay range of $169,000–$211,000 USD, with a midpoint of $190,000. This is a credible range for a staff-level data engineering role in the US and Canadian remote market; the estimated broader annual market range is $180,000–$240,000 USD. ## Core skills Skills and capabilities most closely associated with this opportunity. [Data platform architecture](https://jobicy.com/jobs?search_keywords=Data%20platform%20architecture.md)[AWS serverless](https://jobicy.com/jobs?search_keywords=AWS%20serverless.md)[Snowflake](https://jobicy.com/jobs?search_keywords=Snowflake.md)[dbt](https://jobicy.com/jobs?search_keywords=dbt.md)[Terraform](https://jobicy.com/jobs?search_keywords=Terraform.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[SQL performance tuning](https://jobicy.com/jobs?search_keywords=SQL%20performance%20tuning.md)[ETL and ELT pipelines](https://jobicy.com/jobs?search_keywords=ETL%20and%20ELT%20pipelines.md)[Data orchestration](https://jobicy.com/jobs?search_keywords=Data%20orchestration.md)[Streaming architecture](https://jobicy.com/jobs?search_keywords=Streaming%20architecture.md)[Kinesis](https://jobicy.com/jobs?search_keywords=Kinesis.md)[Docker](https://jobicy.com/jobs?search_keywords=Docker.md)[CI/CD](https://jobicy.com/jobs?search_keywords=CICD.md)[AI/ML data infrastructure](https://jobicy.com/jobs?search_keywords=AIML%20data%20infrastructure.md)[Technical leadership](https://jobicy.com/jobs?search_keywords=Technical%20leadership.md) Sample interview questionsHow would you design a scalable data platform that supports both analytics and AI/ML workloads?I would separate storage, transformation, serving, and orchestration concerns while establishing shared governance and observability standards. I would use an object-store-based data lake for durable raw data, Snowflake for governed analytical workloads, dbt for tested transformations, and purpose-built pipelines for feature, embedding, and training datasets. The design would include lineage, data-quality checks, access controls, cost monitoring, and clear data-product ownership. Describe how you would reduce cost in a Snowflake and AWS data environment without degrading reliability. I would first establish workload-level visibility through warehouse utilization, query profiles, storage growth, pipeline execution metrics, and tagging. I would right-size compute, use auto-suspend and resource monitors, improve inefficient SQL and data models, and separate workloads by service-level needs. In AWS, I would evaluate event batching, Lambda memory and duration tuning, storage lifecycle policies, and architecture choices that reduce unnecessary data movement while preserving operational resilience. What approach would you take to establish engineering standards for a data platform team? I would define a pragmatic baseline covering code review, version control, automated tests, CI/CD, infrastructure as code, documentation, alerting, and incident practices. Standards should be implemented through reusable templates, libraries, and platform guardrails rather than relying only on written policy. I would also measure adoption and use feedback from engineers to improve the developer experience over time. How have you led a technical initiative across teams when you did not have direct authority? I begin by clarifying the business objective, technical constraints, decision owners, and measurable success criteria. I use concise RFCs to make options and trade-offs visible, involve affected teams early, and create an incremental delivery plan that reduces migration risk. Consistent communication, evidence-based decisions, and shared ownership help build alignment without relying on reporting structure. How would you design a reliable event-driven pipeline using AWS services? I would use durable event ingestion with clear schemas, idempotent consumers, replay capability, and explicit dead-letter handling. Services such as Kinesis, Lambda, Step Functions, S3, and DynamoDB can be composed based on throughput and processing requirements, with monitoring for lag, failures, duplicates, and data-quality issues. I would also apply contract testing, backfill procedures, access controls, and infrastructure-as-code deployments to make the pipeline operable at scale. Opportunity details ## About this role. At ClickUp, we’re building the future of work: the first truly converged AI workspace unifying tasks, docs, chat, calendar, and enterprise search, all supercharged by context-driven AI. We are an AI-native company. Every team member is expected to leverage AI daily, and we evaluate AI fluency as part of our hiring process. Join us and help redefine what’s possible. 🚀 We’re looking for a Staff Data Engineer to own the architecture and technical vision of our data platform. This is a high-leverage, high-autonomy role where you’ll set the technical bar for the team, drive cross-functional alignment on data infrastructure strategy, and solve our hardest engineering problems. You’ll operate across AWS serverless technologies, Snowflake, dbt, and Terraform, but your impact goes well beyond any single tool: you’ll shape how we think about reliability, scalability, cost, and developer experience at the platform level. This role is for someone who doesn’t just build great systems, but makes the engineers around them better. The Role: * 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. Qualifications: * 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. Desirable * 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. Equal Opportunity Employer ClickUp is an Equal Opportunity Employer, and qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin. Privacy Notice ClickUp collects and processes personal data in accordance with applicable data protection laws. You can find further details by viewing our [Global Candidate Privacy Notice.](https://clickup.com/careers-terms/candidate-privacy-notice) If you are a Philippine Job Applicant, please also see our [Philippine Data Privacy Notice](https://t333.s.clickup-attachments-stg.com/t333/d9a53ce2-6d2c-48cd-84b1-ed4e525f9613/Philippine%20Data%20Privacy%20Notice_2024.pdf?view=open) for further details. Visa Sponsorship Please note we are unable to sponsor or take over sponsorship of an employment visa for roles outside of engineering and product at this time. Sponsorship for engineering and product roles is not guaranteed, but is instead based on the business needs for that specific role at that time. Please reach out to the recruiter with any questions. Fraud Alert ClickUp Talent Acquisition will only initiate contact via an @[clickup.com](http://clickup.com) email or through our official careers portal on [clickup.com](http://clickup.com). We will never request fees, payments, or sensitive personal information. Please disregard any offers received outside these channels and report them to support@clickup.com. AI Processing Notice ClickUp may use artificial intelligence and machine learning technologies to help review and screen candidates’ employment applications against role-related criteria. These tools support, but do not replace, human decision‑making. If you have questions or need an accommodation in the recruitment process, please contact us at AskPeople@ClickUp.com. 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