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Sr Cloud Engineer

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Onsite

Sr Cloud Engineer

Velodata Global

Sr Cloud Engineer

Skills

SQL QueriesData EngineeringPythonData ScienceKafkaDatabricksData AnalysisMachine LearningAIKubernetesComplianceTerraformAzure

2 months ago

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Important information


Contract type:

Permanent

Location:

Kochi, Kerala, India

Work mode:

Onsite

Published on:

26 August 2026

What they need


Technology: Senior Cloud Engineer

Experience: 6+ Years

Duration: 6 Months

Working Time:- 3:30 PM IST to 11:30 PM IST

Location :- Remote

Critical Deployment Notes

  • Face to Face Connect - Once a candidate is finally selected, they must visit Client s TVM office for a face-to-face interaction. This will be more of a formal/casual discussion rather than an additional interview round. The client will bear the expense.
  • Compliance: For the background verification (BGV) process, PAN card and Aadhaar card of the proposed resources will be required.
  • The UAN number is required for selected candidates.

Senior Cloud Engineer
Azure Infrastructure Databricks Platform

Designation
Senior Cloud Engineer

Experience Required
6+ years (min. 4 years on Azure)

Focus Areas
Azure, Terraform, Databricks Administration

ABOUT THE ROLE
We are seeking a Senior Cloud Engineer to design, implement, and administer the Azure infrastructure behind
our enterprise data and AI platform - centered on Azure Databricks, ADLS Gen2, Azure Data Factory, and Azure
Machine Learning. You will own the platform foundation (infrastructure as code, network and identity
architecture, compute governance, observability, and cost management) and partner closely with our
Databricks administration, data engineering, and data science teams.
This role suits an engineer who enjoys being the technical reference point for a platform - setting architectural
direction, partnering with adjacent teams on complex problems, and translating business requirements into
platform capabilities.
KEY RESPONSIBILITIES

  • Infrastructure as Code (Terraform-first): Author and maintain reusable Terraform modules provisioning the
    Azure data estate - Databricks workspaces, ADLS Gen2, Data Factory, Key Vault, networking, and Azure ML;
    own remote state, module versioning, and drift detection.

  • Network Security Architecture: Design private networking and security posture - private endpoints, hub-
    and-spoke topology, VNet injection, NSGs/firewall rules, managed identities, Key Vault-backed secrets,

RBAC, and data exfiltration controls.

  • Data Lake Infrastructure: Own ADLS Gen2 at the infrastructure layer - storage architecture, hierarchical
    namespace, ACL strategy, lifecycle/tiering, encryption, and access patterns.

  • Observability Reliability: Build and maintain the monitoring layer - Azure Monitor, Log Analytics,
    diagnostics, alerting, and operational runbooks.

  • FinOps Capacity Planning: Own cost visibility and optimization across DBU and storage spend - tagging,
    chargeback/showback, budget alerts, and capacity planning.

  • CI/CD Environment Management: Own deployment pipelines and the promotion path across dev, test,
    and production environments.

  • Databricks Account Workspace Administration: Account console configuration, workspace provisioning
    and topology, admin role delegation, and multi-workspace strategy (day-to-day Databricks operations sit
    with a dedicated admin team; this role sets the standards they work from).

  • Unity Catalog: Metastore design, catalog/schema/table permission models, storage credentials,
    lineage/audit, and Delta Sharing configuration.

  • Identity Access: Entra ID integration, SCIM provisioning, identity federation, service principals, and token
    policy.

  • Compute Governance: Cluster policies, instance pools, autoscaling/autotermination standards, and SQL
    warehouse sizing.

  • Cost Usage Analysis: System tables, usage attribution, and DBU forecasting, with the ability to
    recommend remediation for cost spend.

  • Technical Partnership: Publish standards, reference configurations, and self-service patterns; support data
    teams on cross-cutting issues.

  • Cross-Boundary Diagnostics: Investigate job failures, cluster/connectivity/permission issues, and isolate
    root cause across infrastructure, Databricks, or workload.
    ROLE SCOPE

  • Production ETL/ELT pipeline development and Spark transformation work - owned by the Data Engineering
    team.

  • Model development, training, and tuning - owned by the Data Science team.

  • Dimensional modeling, dbt development, and BI/semantic layer work - outside this role.
    REQUIRED QUALIFICATIONS (MANDATORY)

  • Terraform: Expert-level, hands-on. Authored and maintained production Terraform modules, managed
    remote state, and run IaC through CI/CD - a core daily skill.

  • Databricks Administration: Demonstrable hands-on experience - account/workspace administration, Unity
    Catalog, cluster policies, identity federation, and cost governance.

  • Technical Partnership: Proven track record as senior technical resource to adjacent teams, guiding
    stakeholders from request to requirement and building consensus.

  • Azure Networking Security: Deep knowledge of private endpoints, VNets/hub-spoke design, NSGs, Entra
    ID, managed identities, RBAC, and Key Vault.

  • Data Lake Architecture: ADLS Gen2 design and access control at enterprise scale.

  • Azure Data Factory: Platform-side experience with integration runtimes, managed VNet, credential
    management, and deployment automation.

  • Automation Scripting: Strong Python, plus PowerShell and/or Bash, for platform tooling and automation.

  • Spark SQL Literacy: Sufficient working knowledge to diagnose infrastructure / configuration-level
    performance issues; deep Spark development is not required.
    PREFERRED QUALIFICATIONS

  • Databricks Asset Bundles, the Terraform Databricks provider, and workspace-as-code patterns.

  • Experience leading a Unity Catalog migration or multi-workspace consolidation.

  • Azure Machine Learning, MLflow, or model-serving infrastructure experience.

  • Kubernetes / AKS and containerized workloads.

  • Policy as code - Azure Policy, OPA, Sentinel, or Checkov.

  • Event-driven infrastructure - Event Hubs, Kafka, or Stream Analytics.

  • Formal FinOps practice experience.

  • Multi-region or multi-tenant Databricks deployments.
    PREFERRED CERTIFICATIONS

  • Databricks Certified Data Engineer Professional, or a Databricks platform administrator credential.

  • Microsoft Certified: Azure Solutions Architect Expert (AZ-305).

  • Microsoft Certified: Azure Administrator Associate (AZ-104).

  • Microsoft Certified: DevOps Engineer Expert (AZ-400).

  • HashiCorp Certified: Terraform Associate.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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