Cloud Engineer, Data Platforms

Job Title: Cloud Engineer, Data Platforms

Location: Bangalore

Employment Type: Full Time (Hybrid role)

The job

We are looking for a handson Cloud Engineer with strong expertise in Azure Databricks to accelerate the delivery and operational reliability of AVEVAs data and AI workloads. This role is based in Madrid and is expected to work collaboratively with the local Data Science and AI team as part of normal hybrid working.

The successful candidate will begin with a short induction period within the Cloud Operations team, followed by an embed period, working directly with the Data Science AI team. During this phase, day-to-day priorities will be guided by the Data Science and AI team with a focus on establishing reliable, production-grade data pipelines, hardening platform operations, and ensuring alignment to Cloud Ops standards for security, reliability, and cost efficiency. The role will provide direct support for AI for ALL delivery priorities. After this embed period, the role will transition into the standard Cloud Operations operating model.

This role is ideal for a pragmatic engineer who thrives on making platforms dependable, improving environments, hardening pipelines, automating deployments, and resolving operational issues quickly. It is highly delivery-oriented and requires close collaboration across Cloud Ops, security/governance, and analytics teams to ensure AVEVAs data and AI capabilities run securely, efficiently, and at scale.

Key responsibilities

• Databricks platform engineering (Azure): Build, configure, and operate Databricks workspaces, clusters, policies, and supporting Azure services.
• Production readiness: Enable a clear path to production environments, including operational processes, runbooks, incident response practices, and release readiness.
• Pipeline reliability: Support and harden ETL/ELT pipelines (e.g., Delta Lake patterns), job orchestration, and workload performance tuning.
• Automation CI/CD: Implement infrastructure-as-code and CI/CD practices for Databricks and related Azure resources (e.g., Terraform/Bicep, Azure DevOps/GitHub).
• Security access: Implement least-privilege access, secrets management, identity integration, auditability, and compliance-aligned controls (in partnership with Security / Governance).
• Observability operations: Improve logging, monitoring, and cost controls; diagnose and resolve platform/network issues affecting delivery.
• Embedded delivery support: Participate in Madrid-based team stand-ups and planning; manage an agreed backlog of tasks that unblock AI for ALL and analytics delivery.

Essential requirements

• Azure Databricks expertise: Strong hands-on experience operating Azure Databricks in enterprise settings; able to troubleshoot workspace, networking, and performance issues.
• Engineering discipline: Comfortable with automation, CI/CD, IaC, and operational best practices.
• Data platform fundamentals: Working knowledge of Spark, Delta Lake, Python and SQL; ability to partner effectively with data engineers, AI engineers and data scientists.
• Operational mindset: Bias to reliability and delivery; able to triage issues quickly and communicate clearly across distributed teams.
• Stakeholder collaboration: Works effectively across time zones and teams; able to translate requirements into actionable platform work.
• 5+ years experience in cloud/platform engineering (preferably Azure)
• 2+ years hands-on experience with Databricks (Azure Databricks strongly preferred).
• Candidates who do not meet the required years of experience may still be considered if they possess the following certifications: AZ-900 (Azure Fundamentals) AZ-305 (Designing Azure Infra Solutions) AZ-104,AZ-400(DevOps Focused)and/or AI-102(AI)will be advantageous
• Experience with networking/security patterns in Azure (VNETs, private endpoints, Key Vault, managed identities, etc.).
• Experience with IaC (Terraform, ARM/Bicep) and CI/CD for platform and data workloads.

Desired skills

• Familiarity with AWS/GCP concepts
• Experience with Unity Catalog
• Experience supporting data governance patterns and lineage/metadata tooling
• Familiarity with GenAI workload considerations.