Data Platform / Cloud Infrastructure Engineer (SSR–Senior)

We are looking for a Semi Senior / Senior Data Platform & Cloud Infrastructure Engineer to join a centralized Data Infrastructure team responsible for providing scalable cloud foundations for analytics, machine learning, GenAI and data management workloads.

This role is focused on cloud infrastructure, platform engineering and DevOps practices, working closely with data engineers and ML teams to deploy, maintain and evolve the data platform.
The position is not focused on developing ETL pipelines, but rather on enabling, orchestrating and supporting the infrastructure where those workloads run.

The engineer will collaborate on the internal platform that centralizes data ingestion, orchestration and ML workloads across multiple business units.

Key Responsibilities

Design, deploy and maintain cloud infrastructure for data and ML workloads using Infrastructure as Code.

Manage and evolve AWS-based data platform components running on Kubernetes (EKS).

Provision and maintain services such as EMR on EKS, SageMaker, MWAA (Managed Airflow), Lambda, API Gateway and Step Functions.

Implement and maintain IAM roles, permissions and governance policies aligned with compliance requirements.

Support orchestration frameworks used by data teams (DBT, Airflow, Step Functions).

Collaborate with data engineers to troubleshoot infrastructure or platform issues affecting pipelines.

Participate in platform observability initiatives (metrics, logging and monitoring).

Maintain Terraform modules and deployment pipelines.

Support platform migrations and organizational AWS changes when required.

Contribute to platform reliability, scalability and operational excellence.

Required Qualifications

3+ years of experience working with AWS cloud infrastructure.

Strong experience with Terraform or similar Infrastructure as Code tools.

Experience deploying and operating containerized workloads on Kubernetes / EKS.

Solid understanding of AWS IAM, roles and security best practices.

Experience with serverless architectures (Lambda, API Gateway, Step Functions).

Experience supporting data or ML platforms from an infrastructure perspective.

DevOps mindset and experience managing CI/CD or infrastructure automation.

Strong troubleshooting skills across distributed systems.

Nice to Have (Highly Valued)

Observability experience (Grafana, Prometheus or similar).

Exposure to data ecosystem tools such as DBT, Spark, EMR, Glue or Lake Formation.

Experience with MWAA (Managed Airflow).

Experience deploying or supporting SageMaker environments or MLOps workflows.

Knowledge of QuickSight or BI environments (administrative level).

Experience supporting multi-account AWS environments and organizational migrations.

Familiarity with identity integrations (Okta or similar).

What This Role Is NOT

Not a Data Engineer focused on building ETL pipelines.

Not a BI developer or analytics specialist.

Not a pure backend software engineering role.

This is a platform-focused DevOps / Cloud Infrastructure role inside a Data ecosystem.

Team & Environment

You will be part of a centralized data infrastructure team that provides foundational services for multiple data teams across the organization. The platform supports analytics, machine learning and GenAI workloads and is built primarily on AWS using Kubernetes and Infrastructure as Code.

The team operates with a collaborative approach, working closely with data engineers while owning infrastructure, orchestration and platform reliability.