Key skills
AWS cloud servicesTerraformSQLSnowflakedbtPythonCI/CD workflowsdata qualityobservability best practicesmentoringAIMLLLMELTData EngineeringAnalyticsKafkaAirflowAWSDockerFargateAWS CDKLambdaS3DynamoDBKinesisGitCI/CDMentoring
About this role
ClickUp is redefining the future of work with its innovative AI workspace. The Senior Data Engineer will design and maintain cloud-native data infrastructure and build scalable data pipelines that empower analytics and AI-driven applications.
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
- Experience in both startup and enterprise environments, with ability to adapt to fast-changing priorities while maintaining quality
- Familiarity with orchestration frameworks (Airflow, Dagster, Prefect)
- Knowledge of streaming/event-driven architectures (Kinesis, Kafka)
- Prior experience mentoring or leading engineers