Key Responsibilities
Senior Cloud Infrastructure Engineer - Data Platform
Please source a hands-on contractor whose primary strength is AWS infrastructure and production reliability, with supporting data engineering experience. The role will independently own infrastructure delivery, automation, troubleshooting, and production support, including the infrastructure behind our data platforms.
Must-Have Experience
Candidates should demonstrate 7+ years of relevant infrastructure, SRE, or platform engineering experience, with depth—not just exposure—in:
AWS infrastructure — production ownership of networking, IAM, compute, storage, and databases; practical experience with VPCs, ECS/Fargate, Lambda, S3, and RDS.
Terraform — reusable modules, remote state, environment separation, drift management, and safe changes through reviewed plans and CI/CD.
Production reliability — incident response, root cause analysis, actionable monitoring, alerting, service objectives, and reducing recurring operational issues.
Linux and networking — troubleshooting processes, resource utilization, DNS, TLS, routing, load balancing, and connectivity.
Containers and deployment — Docker, container operations, Git, CI/CD, deployment troubleshooting, and rollback procedures.
Automation — Python and shell scripting for infrastructure operations, diagnostics, and eliminating manual work.
Security and recovery — least-privilege access, secrets management, backups, restoration testing, and disaster recovery.
Performance and cost management — capacity planning, resource tuning, and AWS cost optimization.
Supporting Data Skills
Candidates should have practical experience supporting production data workloads; specialist depth in every data tool is not required.
Redshift and/or Databricks — platform access, connectivity, workload monitoring, and operational troubleshooting.
Airflow and dbt — familiarity with orchestration and transformation workflows; diagnosing failed runs, dependencies, and configuration issues.
SQL and data operations — investigating pipeline failures, checking data freshness, and supporting retries, backfills, and recovery.
Nice-to-Have
Kubernetes/EKS, GitHub Actions, Atlantis, and observability platforms.
Deeper data engineering experience with ingestion, Spark, Iceberg, or Delta Lake.
Retail, ecommerce, ERP, or supply chain systems.
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