Devops & Cloud Engineer (DBA Base)

• Manage, optimize, and monitor SQL (PostgreSQL, MySQL, SQL Server) and NoSQL (MongoDB, Cosmos DB, etc.) databases with a focus on cloud-based PaaS solutions (azure preferred).
• Implement database performance tuning techniques such as indexing, caching strategies, partitioning, and query optimization in self-managed and managed DB.
• Automate database backups, replication, high availability (HA), and failover strategies, Db Health & Maintenance andcluster setups using cloud-native tools.
• Hands on with DB Updates and Patching / Security and Access Control / Data Integrity and Consistency checks.

• Automate infrastructure provisioning using Terraform, and Helm.
• Implement CI/CD pipelines for database changes and schema migrations using Liquibase, etc.
• Implement multi-region database deployments to enhance performance, resilience, and fault tolerance across geographically distributed workloads.
• Manage Kubernetes-based workloads.
• Deploy cloud resources in public cloud environments (GCP/Azure) with full infrastructure lifecycle management.
• Set up monitoring solutions using Prometheus, Grafana, Datadog, or Azure Monitoring.
• Strong Linux administration and shell scripting experience for automation.
• Proficiency in Python and Go programming to automate workflows.
• Support application teams with regular deployments, debugging, root cause analysis (RCA), and advocating best practices.
• Understand the Well-Architected Framework in cloud workloads to optimize and right-size workloads across all pillars, with a strong focus on cost reduction.

• Implement role-based access control (RBAC), encryption strategies, and fine-grained security controls for databases.
• Ensure compliance with GDPR, SOC 2 and other data security regulations and auth methods (basic understanding of concepts, not expert level).
• Kubernetes network security concepts.
• Automate IAM policy enforcement, database firewall rules, and security auditing for sensitive datasets.

Monitoring, Logging & Troubleshooting

Enable real-time application monitoring by creating observability visualizations using Prometheus, Loki, and Grafana to track system health, logs, metrics, and infrastructure performance.

• Proactively troubleshoot and resolve database and support cloud application-related issues.

• Work closely with SRE, DevOps, operations and Engineering teams to align infrastructure enabling application teams to run cloud-native applications effectively at scale.
• Document system architectures, performance benchmarks, and operational playbooks.
• Enable development teams on efficient data modelling, query optimization, and best DevOps practices for database management along with compute resource management (cloud compute services).
• Basic understanding of Java Spring microservices to work with application teams.

Qualifications & Skills

Desired Skills (Must-Have)

• 7+ years of industry experience, with 4+ years in DevOps focusing on database administration & performance tuning frameworks.
• Expertise in SQL (Azure SQL Database, PostgreSQL Flexible Server, MySQL, SQL Server) and NoSQL (Cosmos DB)
• Familiarity with Kafka and message queuing systems.
• Experience with code deployment procedures and GitOps Flux, with advanced knowledge of canary and blue-green deployment strategies.
• Infrastructure as Code (IaC) & Automation:
• Terraform, Kubernetes (AKS), Helm, and CI/CD tools (Azure DevOps, GitHub Actions)
• Database Performance Optimization:
• Query tuning, indexing, caching, connection pooling, and query profiling for Azure database services and self-managed Databases.
• Monitoring & Observability:
• Experience with Azure Monitor, Application Insights, Log Analytics, and Grafana for health checks and dashboards.
• Scripting & Automation:
• Proficiency in Python and Go for database automation and DevOps scripting.
• Certifications on cloud associate level or +, on Kubernetes (CKA, CKAD) are well addons and knowledge on any public cloud
• Basic data engineering knowledge, exposure to ADF, Spark, and Airflow for data pipelines, and experience in ML model deployment (Azure ML, MLflow). Bonus if eager to learn ML visualization, Vector DBs, Power BI, and Apache Superset