Senior DevOps & Data Platform Engineer (Hybrid, AWS)

Tidal; Buenos Aires; Híbrido; ARS 2.000.000 - 4.500.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Flexible work environment

Comprehensive benefits

Annual wellness days

Global collaboration and networking

Descripción de la vacante

Proofpoint in Córdoba, Argentina is seeking a Senior DevOps and Data Engineer to join our Data Platform team. This hybrid role sits at the intersection of platform engineering and data infrastructure, owning reliability, automation, and the delivery of the systems powering data pipelines and analytics.

You will build and maintain cloud infrastructure on AWS, CI/CD pipelines, and DevOps tooling to enable data teams to ship high-quality data products faster and more safely.

Formación

• 5+ years of professional experience in DevOps, platform engineering, or site reliability engineering, with at least 2 years of hands-on data engineering work.
• Deep expertise with AWS core services: S3, IAM, VPC, CloudWatch, Lambda, ECS/EKS, Secrets Manager, and CloudFormation/CDK.
• Hands-on experience with AWS data services: Glue (ETL jobs, Crawlers, Data Catalog), Athena (workgroup management, query tuning, federated queries), and S3 (lifecycle, encryption, event notifications).
• Proficiency with Infrastructure-as-Code tools-Terraform and/or AWS CDK-for managing production cloud environments.
• Strong CI/CD experience: designing and maintaining pipelines in GitHub Actions, Jenkins, GitLab CI, or equivalent.
• Experience with container technologies (Docker) and container orchestration (Amazon ECS, EKS, or Kubernetes).

Responsabilidades

• Design, implement, and maintain CI/CD pipelines (GitHub Actions, Jenkins, or equivalent) for automated testing, deployment, and promotion of AWS Glue ETL jobs, Athena views, and related data infrastructure.
• Build and manage cloud infrastructure on AWS using Infrastructure-as-Code (Terraform and/or AWS CDK)-covering S3 buckets, IAM roles, Glue resources, Athena workgroups, Lake Formation permissions, VPCs, and supporting services.
• Own the observability stack for data pipelines: define and instrument metrics, logs, and alerts (CloudWatch, Datadog, or equivalent) to ensure SLA compliance and fast incident response.
• Develop and maintain the DataOps platform-self-service tooling, pipeline templates, and shared libraries that allow data engineers to spin up production-grade pipelines quickly.
• Manage container and compute infrastructure (Docker, ECS/EKS, or Lambda) used to support data ingestion microservices and orchestration workers.
• Design and operate workflow orchestration environments (Amazon MWAA / Apache Airflow, AWS Step Functions) including upgrades, scaling, and reliability hardening.
• Build and maintain AWS Glue ETL jobs (PySpark) for large-scale data ingestion and transformation across the data lake on S3.
• Drive security best practices across the data platform: secrets management (AWS Secrets Manager / Parameter Store), least-privilege IAM, network segmentation, and compliance controls.
• Collaborate with data engineers, data scientists, and security teams to translate requirements into scalable, automated infrastructure solutions.
• Lead incident response for data platform outages: on-call participation, root-cause analysis, and post-mortem-driven improvements.
• Mentor junior engineers on DevOps and data engineering practices, conduct code and design reviews, and contribute to architecture decision records.

Conocimientos

AWS expert

Data engineering

DevOps mindset

Observability focus

Strong communicator

Herramientas

Docker

Kubernetes

GitHub Actions

Jenkins

Terraform

CDK

Descripción del empleo

Proofpoint in Córdoba, Argentina is seeking a Senior DevOps and Data Engineer to join our Data Platform team. This hybrid role sits at the intersection of platform engineering and data infrastructure, owning reliability, automation, and the delivery of the systems powering data pipelines and analytics.

You will build and maintain cloud infrastructure on AWS, CI/CD pipelines, and DevOps tooling to enable data teams to ship high-quality data products faster and more safely.