ShareForce
DevOps Engineer
ShareForceUnited Kingdom2 days ago
Full-timeRemote FriendlyEngineering, Information Technology
The DevOps Engineer plays a critical role in enabling scalable, reliable, and automated data solutions across the Azure ecosystem. This position focuses on supporting end-to-end data and ML pipelines, primarily built on Azure

DevOps, Azure Databricks, and modern Infrastructure-as-Code (IaC) practices using Bicep or Terraform.

The candidate collaborates closely with our client's Data Engineers, Security Team and Platform Teams to ensure smooth development workflows, automated deployments, and securely governed cloud environments all according to our client's Quality standards.

Key Responsibilities

CI/CD Pipeline Engineering (Azure DevOps)

  • Design, develop, and maintain Azure DevOps pipelines for data
  • processing workflows, ML model training, and Databricks deployments.
  • Implement pipeline quality gates, automated testing, environment
  • promotion strategies, and artifact management.
  • Ensure pipelines are resilient, observable, and aligned with
  • organizational standards.
  • Ensure the Software Development Lifecycle is built up according to the
  • client's Policies and meet their requirements.

Infrastructure as Code (IaC)

  • Build, maintain, and standardize cloud infrastructure using Bicep or Terraform for Azure resources such as Databricks Workspaces, Storage Accounts, Key Vaults, Networks and containerized where applicable
  • Ensure infrastructure is modular, reusable, and compliant with enterprise security and governance requirements.

Automation & Scripting (PowerShell, Python)

  • Develop automation for recurring operational tasks (orchestration, monitoring, environment provisioning) using PowerShell and Python.
  • Create scripts supporting Databricks job deployments, cluster lifecycle management, ML model registration, and data workflow automation.

Databricks & Machine Learning Operations (MLOps)

  • Build and maintain deployment mechanisms for Databricks notebooks/Jobs, workflows, ML models, and Delta pipelines.Support ML lifecycle automation, including data validation, model packaging, model registry updates, and automated retraining pipelines.- Collaborate with Data Science teams to operationalize machine learning workflows in production environments.

Cloud Environment Management & Reliability

  • Ensure high availability, scalability, and reliability of data and ML workloads in Azure.
  • Implement monitoring & alerting for pipelines, clusters, and data workflows.
  • Contribute to operational improvements and proactive issue prevention.

Collaboration & Governance

  • Work closely with cross-functional teams and participate in review processes for IaC, pipeline changes, and data platform enhancements.
  • Ensure changes follow standardized processes as outlined in the client's Quality Handbook
  • Document designs, processes, and architecture diagrams to support transparency and long-term maintainability.

Required Skills & Experience

Technical Skills

  • Azure DevOps pipelines (YAML), environments, approvals, artifacts
  • Infrastructure as Code: Bicep and/or Terraform and/or Pulumi Scripting Languages: PowerShell and Python
  • Strong understanding of Azure Databricks, Spark fundamentals, and Databricks deployment patterns
  • Experience with Azure Core Services: Key Vault, Storage, VNet, AAD, Monitor, AKS (optional)
  • Familiarity with containerization, Git branching strategies, and DevOps best practices
  • Experience with MLOps frameworks (MLflow, Databricks Model Registry)
  • Experience deploying large-scale data or ML workloads
  • Knowledge of cloud security best practices and networking in Azure

Soft Skills

  • Strong communication and ability to collaborate across data, engineering, and infrastructure teams
  • Analytical mindset with a passion for automation and continuous improvement
  • Ability to troubleshoot complex distributed systems and data pipelines

Additional Information

  • Rate offered: £450-475 per day
  • IR35 Status: Outside
  • Location: Remote
  • Start date: March '26
  • Duration: 3 months initial sign up with significant opportunity for extension.

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