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SeniorFull-timePythonAWSDockerTerraformData ScienceCI/CDGitAIML
Salary Range
$132,600 - $179,400 /year
This salary is estimated based on similar roles. The actual salary may vary.
About the Role
We are looking for a Senior Data Engineer to help shape and scale our modern data platform at ClickUp. This role combines hands-on technical ownership with strategic influence across our data ecosystem, working with AWS serverless technologies, Snowflake, dbt, and Terraform to design reliable, scalable, and cost-effective data pipelines that empower analytics and AI-driven applications.
Key Responsibilities
- →Design, build, and maintain cloud-native data infrastructure using Terraform for IaC
- →Develop and optimize data pipelines leveraging AWS services (Lambda, Fargate, Step Functions, S3, Kinesis, DynamoDB, Aurora, etc.) and Snowflake
- →Implement ELT workflows within dbt in partnership with our Analytics Engineering function
- →Build and maintain LLM frameworks, ensuring high-quality and cost effective outputs
- →Automate infrastructure and pipeline deployments with CI/CD best practices
- →Monitor, debug, and improve system performance with strong observability and logging practices
- →Partner with cross-functional teams (analytics, data science, product, engineering) to deliver high-quality data solutions
- →Mentor teammates and contribute to engineering standards, raising the technical bar across the team
Requirements
- ✓5+ years of professional experience in data engineering or software engineering, with emphasis on scalable data systems
- ✓Deep experience with AWS cloud services (especially Lambda, Fargate, Step Functions, S3, DynamoDB, Aurora)
- ✓Hands-on expertise in Terraform and/or AWS CDK for managing infrastructure as code
- ✓Strong knowledge of SQL and experience with Snowflake or another cloud data warehouse
- ✓Proficiency with dbt and modern ELT patterns
- ✓Solid coding skills in Python (or another general-purpose language)
- ✓Experience building pipelines to support AI/ML workflows (feature engineering, training data pipelines, model monitoring)
- ✓Understanding of CI/CD workflows, Git-based development, and containerization (Docker)
- ✓Knowledge of data quality, governance, and observability best practices
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