ClickUp5 дней назад

Staff Data Engineer

Полная занятостьУдалёнка

Обязанности

01Own the technical architecture of ClickUp's data platform, making design decisions that balance scalability, cost, reliability, and velocity.
02Define and drive the technical roadmap for data infrastructure in partnership with leadership.
03Design systems at scale: build frameworks, abstractions, and patterns that other engineers use daily.
04Lead complex, cross-team technical initiatives spanning data engineering, analytics engineering, data science, and data analytics.
05Drive cost optimization across cloud infrastructure and compute, turning efficiency into a competitive advantage.
06Build and evolve our data pipelines using AWS serverless (Lambda, Fargate, Step Functions, Kinesis, S3, DynamoDB, Aurora), Snowflake, and dbt.
07Establish and champion engineering standards: observability, testing, CI/CD, code review, and documentation practices.
08Design and maintain infrastructure for AI/ML workloads, including LLM frameworks, feature pipelines, training data systems, and model monitoring.
09Mentor senior engineers, provide technical guidance through design reviews, and raise the overall engineering quality of the team.
10Influence org-wide technical decisions and represent data engineering in company-level architecture discussions.

01Significant professional experience in data engineering or backend/infrastructure engineering, with at least 3 years operating at a senior or staff level.
02Proven track record of owning architecture for data platforms or large-scale distributed systems.
03Deep expertise in AWS cloud services (Lambda, Fargate, Step Functions, S3, Kinesis, DynamoDB, Aurora) and infrastructure as code (Terraform and/or CDK).
04Expert-level SQL and Snowflake (or equivalent cloud data warehouse) knowledge, including performance tuning and cost optimization.
05Strong experience with dbt and modern ELT/ETL patterns at scale.
06Advanced Python skills with emphasis on building reusable libraries, frameworks, and tooling.
07Hands-on experience with orchestration frameworks (Airflow, Dagster, or Prefect) in production environments.
08Experience building data infrastructure for AI/ML: feature stores, training pipelines, embedding pipelines, model serving, or LLM integration.
09Deep understanding of streaming and event-driven architectures (Kinesis, Kafka, or equivalent).
10Mastery of CI/CD, Git workflows, containerization (Docker), and deployment automation.
11Strong communication skills: ability to write technical RFCs, influence without authority, and translate complex trade-offs for non-technical stakeholders.
12Track record of mentoring and growing engineers, with a multiplier mindset.