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.

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.

Senior Data Engineer

Key skills