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Palo Alto Labs

Data Engineer- DataBricks

Posted 10 Days Ago
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In-Office
Pune, Maharashtra, IND
Senior level
In-Office
Pune, Maharashtra, IND
Senior level
Designs and operates Databricks data platforms, pipelines, workflows, CI/CD processes, and cloud infrastructure on Azure. Responsibilities include cluster and workspace management, automated testing, Infrastructure as Code, access controls, FinOps, Airflow orchestration, monitoring, incident response, and reusable Terraform and Databricks templates. The role also improves platform reliability, security, developer productivity, and production code promotion processes while partnering with engineering, architecture, and security teams.
The summary above was generated by AI

Role: Data Engineer
Location:
Remote
Employment Type: Full-Time
Experience: 4-7 years

 

KEY RESPONSIBILITIES

·       Design and implement CI/CD pipelines for data pipelines (ingestion jobs) and transformation projects (e.g., dbt on Databricks, SQL, notebooks)

·       Orchestrate Databricks jobs and workflows end-to-end (ingestion, transformation, quality checks)

·       Integrate automated testing into CI/CD, including schema and contract checks for data models and tables

·       Implement FinOps best practices to support cost monitoring and allocation across the EDP

·       Automate platform operations for Databricks and related services, such as workspace and cluster provisioning, library and runtime mgmt. and job deployment/config

·       Implement and maintain identity and access management for Databricks and supporting cloud resources, including workspace- and cluster-level permissions, table- and view-level access controls (e.g., Unity Catalog or equivalent), service principals, groups, and roles for automated workloads, and RBAC & TBAC models in collaboration with EDP Architect

·       Provide patterns, templates, and reusable modules (Terraform modules, Airflow DAG patterns, Databricks job templates) to accelerate onboarding of new projects

·       Continuously evaluate and improve tooling, pipelines, and platform architecture to increase reliability, security, and developer productivity on Databricks

·       Define the code promotion process to minimize impacts across domains as code is promoted to production

·       Manage end-to-end orchestration using managed Airflow. Contribute to defining and tracking SLA/SLO/SLIs for the platform and participate in incident response (triage, root cause analysis)

·       Practical knowledge of IT Infrastructure technologies, cloud computing Azure), cybersecurity, and disaster recovery.

·       Working knowledge of Azure ecosystem, including hands-on experience designing, building, and optimizing scalable data pipelines within cloud-native environments.

QUALIFICATIONS

·       Hands-on experience with Databricks in production environment, including workspace and cluster management, jobs/workflows and integrations with orchestration tools

·       Strong experience with CI/CD pipelines (e.g., GitHub Actions, GitLab CI, Azure DevOps, or similar) and Git-based workflows.

·       Strong experience with Infrastructure as Code (IaC) and orchestration tools for provisioning and managing Databricks and cloud infrastructure.

·       Experience implementing automated tests and quality gates in CI/CD pipelines.

·       Ability to partner effectively with data engineers, analytics engineers, architects, and security teams.

MINIMUM EXPERIENCE & EDUCATION

·       Bachelor’s degree in Computer Science, Information Technology, or related field preferred, or equivalent work experience.

·       4–7+ years in DevOps, Cloud Engineering, Site Reliability Engineering, or Platform Engineering, with at least 2+ years supporting data/analytics platforms.

·       Experience operating production data workloads, including monitoring, logging, performance tuning, and incident response.

·       Scripting skills (e.g., Python, Bash, PowerShell) for automation and integration.

·       Experience in CPG, retail, manufacturing, or distribution environments preferred.

 

 



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