W2 Platform (DevOps) Engineer

Title: Platform (DevOps) Engineer
Location: US (Remote)

Responsibilities

Design, build, and manage scalable AWS infrastructure including EC2, EKS, Lambda, API Gateway, VPCs, IAM, Security Groups, Load Balancers, and routing components
Implement Infrastructure-as-Code (IaC) using Terraform/AWS CDK to automate provisioning, scaling, and environment management
Set up and manage DevOps pipelines using GitHub, GitHub Actions, CodeBuild, CodeDeploy and ArgoCD to support automated build, test, and deployment workflows.
Design, deploy, and manage Databricks workspaces, clusters, cluster policies, Unity Catalog, networking, and security integrations on AWS.
Partner with data engineering teams to enable reliable execution of pipelines, data ingestion frameworks, batch processing, and workflow orchestration.
Automate deployment and lifecycle management of data platform components, including Databricks jobs, workflows, libraries, secrets, service principals, and environment configurations.
Collaborate with data engineering teams to support infrastructure components such as AWS Glue, Lambda, Databricks on AWS, DynamoDB, and Redshift.
Monitor and optimize platform performance, reliability, and cloud costs for data engineering workloads and analytics platforms. Troubleshoot platform, connectivity, and workload issues
Utilize AWS Well-Architected Framework principles to design secure, reliable, cost-efficient, and high-performing solutions
Implement platform governance, access controls, security standards, and operational best practices across cloud and data platforms.
Contribute to establishing DevOps best practices, reusable templates, and standard operating procedures across engagements

Qualifications

Strong hands-on experience managing AWS infrastructure through Terraform, including networking, security, compute, storage, and IAM services.
Practical experience working with Databricks on AWS, including workspace administration, compute management, job orchestration, and platform operations.
Good understanding of data engineering concepts such as ETL/ELT pipelines, data lakes, workflow orchestration, batch processing, and data platform architecture.
Working knowledge of data engineering ecosystems and tools (e.g., Databricks, AWS Glue, Redshift).
Practical experience designing and maintaining CI/CD pipelines.
Solid understanding of cloud architecture patterns, DevOps workflows, automation, and monitoring practices.

Nice to Have
Experience implementing container orchestration using Kubernetes / EKS.
Knowledge of cost optimization, FinOps practices, and cloud governance.
Exposure to multi-account AWS setups using Control Tower, Organizations, or Landing Zone.
Familiarity with logging and observability tools such as CloudWatch, Grafana, Prometheus, or ELK.
Experience working with cross-functional agile teams in a consulting environment.