Data Engineering & Infrastructure Lead Id88014

Agileengine; San Carlos de Bariloche; On-site; ARS 135,892,000 - 241,586,000; Part time

Benefits offered by this job

Professional growth

Competitive USD-based compensation

A selection of exciting projects

Flextime

Job summary

AgileEngine is seeking a part-time Data Engineering and Infrastructure Lead to audit AWS architecture, CI/CD processes, and ETL decisions in an advisory capacity. This role weighs managed tooling against custom builds, reviews SageMaker-based ML pipelines, and mentors the team on engineering standards.

You will evaluate AI-assisted development workflows (e.g., Claude Code) and guide the data-ingestion architecture while reporting findings to leadership with clear risk framing.

Qualifications

• 6+ years of hands-on AWS experience (ECS/Fargate, ECR, RDS, S3, SageMaker).
• Fluency in Infrastructure-as-code using Terraform.
• Proven track record designing/auditing CI/CD pipelines and SDLC governance.
• Strong data engineering depth for ETL/ingestion architecture decisions.
• Experience evaluating AI-assisted development workflows (Claude Code) for quality and cost governance.
• Comfortable in a part-time advisory capacity (50% to 25%) and mentoring engineers.
• Upper-intermediate English level.

Responsibilities

• Audit AWS architecture across live apps (MAT, Signal IQ, Impact Engine) including ECS/ECR, RDS, S3, VPC, CloudFront, SageMaker ML ops.
• Review CI/CD processes and lifecycle; recommend SDLC governance (schema versioning, environment separation, release gating).
• Evaluate Claude Code usage for code generation and reviews; ensure engineering standards.
• Provide early technical opinions on ETL/data ingestion architecture (managed tools vs. custom build).
• Mentor and accelerate day-to-day infra build team focusing on AWS, DevOps, CI/CD, and data engineering practices.
• Set recommendations and technical direction from audit findings; report to leadership with risk readiness.

Skills

AWS expertise

Terraform

CI/CD governance

Data engineering

AI/LLM workflows

Mentoring & communication

English (upper-intermediate)

Tools

SageMaker

ECS/Fargate

ECR

RDS (Postgres)

S3

VPC/CloudFront

IAM

Job description

Job Description

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you! :)

ABOUT THE ROLE

We are looking for a part-time Data Engineering and Infrastructure Lead to audit AWS architecture, CI/CD processes, and ETL pipeline decisions in an advisory capacity. This person weighs managed tooling against custom builds, reviews SageMaker-based MLOps pipelines, and mentors the team on engineering standards. Evaluating AI-assisted development workflows is part of the role.

WHAT YOU WILL DO

• Audit the current AWS architecture across live applications (MAT / Signal IQ and the Impact Engine), 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.

MUST HAVES

• 6+ 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.

NICE TO HAVES

• 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.

PERKS AND BENEFITS

• Professional growth: Accelerate your professional journey with mentorship, TechTalks, and personalized growth roadmaps.
• Competitive compensation: We match your ever-growing skills, talent, and contributions with competitive USD-based compensation and budgets for education, fitness, and team activities.
• A selection of exciting projects: Join projects with modern solutions development and top-tier clients that include Fortune 500 enterprises and leading product brands.
• Flextime: Tailor your schedule for an optimal work-life balance, by having the options of working from home and going to the office – whatever makes you the happiest and most productive.

Requirements

• 6+ 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.