Join our Infrastructure & Platform Engineering team to automate cloud infrastructure, CI/CD pipelines, and containerized services across AWS, GCP, and private cloud environments. You will drive automation, enhance platform reliability and security, and gain hands-on experience with our next-generation AWS Lakehouse ecosystem, including Databricks, Airbyte, Apache Iceberg, Polaris, and Cube
Job Responsibilities:
- Assist in provisioning, configuring, and managing multi-cloud infrastructure across AWS, GCP, and Private Cloud / on-premises environments using Terraform.
- Maintain modular, reusable, version-controlled IaC codebases adhering to enterprise security baselines and least-privilege principles.
- Support virtual networks, VPC peering, cloud interconnects, IAM role boundaries, and hybrid connectivity.
- Build, optimize, and maintain automated CI/CD build and delivery pipelines using Bitbucket Pipelines and self-hosted Bitbucket Runners.
- Package application workloads and platform services into standardized, secure, and lightweight container images (Docker).
- Manage Kubernetes cluster deployments and workload configurations using ArgoCD following GitOps paradigms.
- Facilitate seamless environment promotion (Dev, Staging, Production), configuration drift detection and automated rollback workflows.
- Support deployment, orchestration, and infrastructure automation for our AWS-based Modern Data Lakehouse platform.
- Assist in configuring and operating Apache Iceberg table storage formats and metadata layers on AWS S3.
- Maintain and govern metadata registries with Apache Polaris catalog for cross-engine Iceberg interoperability.
- Support Databricks workspace lifecycle, cluster provisioning policies and Unity/cloud IAM integrations.
- Manage Airbyte deployment infrastructure (scaling worker pools, orchestrating connectors and scheduling ELT ingestion).
- Operate Cube semantic layer instances, pre-aggregation caching stores and BI connection interfaces.
- Assist in monitoring platform health, cluster capacity and data pipeline throughput using observability and monitoring tools (TrueWatch, Prometheus, Grafana, Datadog, AWS CloudWatch).
- Support incident troubleshooting, root cause analysis (RCA) and service reliability improvements.
- Implement security controls including container vulnerability scanning, secrets management (AWS Secrets Manager / Vault), and security audits.
- Support annual VAPT, PCI DSS, IA assessments and external audits.
- Work with stakeholders to ensure platform reliability, security and operational excellence.
- Use Generative AI and automation to redesign workflows, reduce repetitive work and human error, improve decision quality, and build the team's strategic consulting capability
- Any ad hoc duties as assigned
Job Requirements:
- Bachelor’s degree in computer science, Software Engineering, Information Technology, STEM disciplines, or equivalent practical experience and relevant technical certifications
- Candidate with strong DevSecOps, Cloud Engineering, Systems Operations or IT Infrastructure is preferred
- Solid understanding of Linux/Unix operating systems, shell scripting (Bash) and core networking principles (TCP/IP, DNS, VPNs, Subnets, routing).
- Assist in monitoring platform health, cluster capacity and data pipeline throughput using observability and monitoring tools (TrueWatch, Prometheus, Grafana, Datadog, AWS CloudWatch).
- Familiar with setting up, maintaining or auto-scaling self-hosted Bitbucket Runners in private cloud or VPC setups.
- Practical coursework or hands-on experience with at least one major public cloud platform (AWS preferred, or GCP), with exposure to high availability architectures, disaster recovery planning and cost optimization best practices.
- Strong working knowledge of AWS S3 object storage optimization, partitioning schemes and lifecycle rules.
- Familiar with Terraform for infrastructure as code (IaC), including declarative cloud resource provisioning and infrastructure management
- Solid understanding of Docker containerization, Kubernetes fundamentals and GitOps principles.
- Familiar with tools such as ArgoCD, Bitbucket Pipelines, GitHub Actions, or GitLab CI/CD for automated application delivery and deployment
- Hands-on experience or familiarity with Kubernetes (EKS, GKE, or AKS), including workload deployment, Helm chart management, service mesh implementation (Istio/Linkerd and container security and operational best practices will be plus
- Proficient in Python, Go, Ruby, Bash for building custom tooling and automating complex operational workflows
- Hands-on exposure to or genuine curiosity in Lakehouse technologies: Databricks, Apache Iceberg, Polaris Catalog, Airbyte, or Cube is preferred
- AWS Certified Solutions Architect / SysOps Administrator, HashiCorp Certified: Terraform Associate or Databricks Certified Associate will be an advantage
