Data Engineer II

Design, build, and optimize scalable ETL/ELT pipelines across AWS(Aurora, Redshift, Glue) that keep data flowing reliably to our engineering, analytics, and product teams — powering the AI-assistedinsights behind Detail Page's patented content optimization platform.

helps Amazon brands grow organic traffic throughcontent optimization and market segment share insights, using apatented (US Patent No. 12,093,340), AI-assisted process to identify high-traffic keywords and rewrite product content for sustainableorganic growth. We're looking for a Data Engineer to build and optimize scalable data pipelines, support our ETL/ELT and APIprocesses, and keep data flowing seamlessly across our organization and to our end users — working closely with engineering, analytics,and product.

• Design, develop, and maintain scalable data pipelines using avariety of AWS services
• Work with relational databases such as Aurora (PostgreSQL),Redshift, and AWS Glue for large-scale data management
• Develop and optimize ETL/ELT processes for smooth data integration from multiple sources
• Collaborate with cross-functional teams to ensure data accessibility and integrity across platforms
• Use Python, SQL, and Pandas for data manipulation, analysis, and workflow automation
• Implement unit testing (PyTest) to ensure code reliability
• Manage API integrations and development using FastAPI
• Assist with infrastructure as code (IaC) using CDK and Terraform
• Build CI/CD pipelines in Jenkins

• SQL mastery – expertise in complex SQL queries and database optimization
• 2-3 years of experience in Data Engineering with AWS and Python
• Linux proficiency – comfortable with shell scripting, common CLItools, and building/testing Linux-based Docker images
• Advanced Python – strong hands‑on experience, ideally with Data Engineering tools like PySpark, Pandas, and SQL Alchemy
• Git proficiency – comfortable with version control, branching,and collaboration
• ETL/ELT experience – proven ability to build and optimizeextract, transform, and load processes
• Unit testing – familiarity with PyTest or similar tools

• AWS experience with Aurora (PostgreSQL or MySQL), Redshift, Fargate,Lambda, SQS, and IAM, plus tools like Boto3 and the AWS CLI
• Airflow expertise, particularly AWS Managed Airflow, for scheduling and orchestrating data workflows
• API development experience with API Gateway and FastAPI
• Infrastructure as Code experience with CDK or Terraform
• Familiarity with caching tools like Redis
• Basic Dev Ops knowledge for CI/CD pipelines and dev workflow optimization
• Familiarity with DBT

Compensation and equity details to be finalized by hiring team.