[Hiring] Senior Data Engineer @AgileEngine

Role Description

We are looking for a Senior Data Engineer to review the AWS architecture, CI/CD, and data pipelines of a media analytics platform, and mentor the engineering team in a part-time advisory role.

Qualifications

• 4+ years of deep, current hands-on experience with AWS: ECS/Fargate, ECR, RDS (Postgres), S3, SageMaker, EventBridge, VPC/ALB/CloudFront, IAM.
• Fluency in Infrastructure-as-code, specifically Terraform.
• Proven track record designing or auditing CI/CD pipelines and SDLC governance (e.g., Alembic for schema/version control, environment separation, release gating).
• Strong data engineering depth to provide defensible opinions on ETL / ingestion architecture (managed tools vs. custom builds).
• Experience evaluating AI-assisted / LLM-assisted development workflows (e.g., Claude Code) from an engineering-quality and cost-governance standpoint.
• Comfortable operating in a part-time advisory capacity (50% stepping down to 25%) as a strong communicator who can mentor less experienced engineers.
• Upper-intermediate English level.

Requirements

• Prior experience working with small, fast-moving engineering teams.
• Understanding of MLOps pipelines and data science infrastructure.
• Media/AdTech domain knowledge is helpful but not required.

What You Will Do

• Audit the current AWS architecture across live applications, including ECS/ECR, RDS (Postgres), S3, VPC, CloudFront/SSO, and SageMaker-based MLOps pipeline design.
• Review the CI/CD process and end-to-end app development lifecycle, recommending SDLC governance layers (schema versioning, environment separation, release gating).
• Evaluate how the team uses Claude Code (PR generation, automated reviews, token/cost management) to confirm output meets professional engineering standards.
• Provide an early technical opinion on ETL / data-ingestion pipeline architecture (managed ELT tooling vs. custom build).
• Mentor and accelerate the day-to-day infrastructure build team, focusing on AWS, DevOps, CI/CD, and data engineering practices.
• Set out recommendations and technical direction where the audit surfaces necessary changes to the current build.
• Report findings and recommendations directly to leadership, framing high-level risk and readiness.

Benefits

• Professional growth: Mentorship, TechTalks, and personalized growth roadmaps.
• Competitive compensation: USD-based pay with education, fitness, and team activity budgets.
• Exciting projects: Modern solutions with Fortune 500 and top product companies.
• Flextime: Flexible schedule with remote and office options.