Specialist, Databricks Platform Engineer

Overview

Support the administration, operation, monitoring, and continuous improvement of an enterprise Databricks platform, working closely with platform teams, Data Engineers, Data Scientists, and other technical users.

What you'll do

• Support day-to-day administration and operation of Databricks environments.
• Manage workspaces, compute resources, clusters, SQL Warehouses, jobs/workflows, libraries, and workspace configurations.
• Support user provisioning, permissions, access controls, service principals, and Unity Catalog.
• Monitor platform health, workload performance, utilization, and operational issues.
• Investigate platform and workload failures using logs, job history, compute metrics, and other diagnostic information.
• Help identify whether issues originate from compute, configuration, permissions, data, application code, or other dependencies.
• Assist with platform upgrades, configuration changes, and operational improvements.
• Maintain platform documentation, troubleshooting guides, and operational procedures.
• Support data engineering workloads running on Databricks using Spark, PySpark, Python, SQL, Delta Lake, and Databricks Jobs/Workflows.
• Assist teams in troubleshooting failed or poorly performing data pipelines.
• Review job failures, logs, Spark execution behavior, compute utilization, data issues, and dependencies to help identify root causes.
• Work with Data Engineers to troubleshoot ingestion, transformation, scheduling, and processing issues.
• Assist with identifying performance bottlenecks and opportunities to improve workload reliability and efficiency.
• Understand basic Data Lake and Lakehouse architecture concepts and how they relate to Databricks workloads.
• Monitor the health and reliability of Databricks workloads and platform services.
• Help develop and maintain monitoring and dashboards for job and pipeline failures, compute utilization, workload performance, platform health, capacity and usage, and cost and consumption.
• Assist in configuring alerts and identifying recurring failure patterns.
• Participate in troubleshooting incidents and root-cause analysis.
• Help translate recurring incidents into opportunities for automation and platform improvements.
• Work with enterprise monitoring solutions such as CloudWatch, Splunk, Datadog, Grafana, or similar technologies.
• Work directly with Data Engineers, Data Scientists, analysts, and other Databricks users to understand and troubleshoot technical issues involving notebooks, jobs, libraries, compute, permissions, data access, Python/SQL environments, and Spark workloads.
• Guide users through appropriate troubleshooting steps and escalate complex platform issues when needed.
• Document common issues and develop reusable knowledge articles and troubleshooting guides.
• Communicate technical issues clearly to both platform teams and end users.
• Provide a positive and collaborative support experience.
• Support Databricks environments used by Data Science and analytics teams, including environment, library, compute, permissions, and data access issues.
• Support the configuration and operation of compute environments used for Data Science and ML workloads.
• Develop working knowledge of emerging AI/ML capabilities available within the Databricks ecosystem.
• Stay informed about new Databricks features, runtimes, administrative capabilities, and platform improvements.
• Experiment with relevant new capabilities in development or test environments and participate in small proofs-of-concept.
• Share findings with the broader platform team.
• Identify opportunities to automate repetitive platform administration and support activities.
• Continuously improve technical skills across Databricks, cloud, Data Engineering, and Data Science technologies.

What you'll need

• Bachelor's or master's degree in computer science, Engineering, Data Science, or related field.
• 3-5 years of experience in Data Engineering, Data Platform Administration, or Cloud Data Platforms.
• Experience with Unity Catalog, Delta Lake, Databricks Jobs/Workflows, and SQL Warehouses.
• 1-2 years of hands-on Databricks administration experience.
• Experience working with Databricks or similar cloud-based data platforms.
• Understanding of Databricks administration and platform operations.
• Working knowledge of Python and SQL.
• Familiarity with Apache Spark / PySpark and distributed data-processing concepts.
• Understanding of Data Engineering and ETL/ELT concepts.
• Experience troubleshooting technical issues using logs, monitoring information, and application/platform diagnostics.
• Familiarity with cloud environments, preferably AWS.
• Understanding of access management, security, and data governance concepts.
• Ability to communicate effectively with technical users and work collaboratively to investigate issues.
• Familiarity with Git, CI/CD, GitHub Actions, or similar DevOps practices.
• Exposure to Infrastructure as Code technologies such as Terraform.
• Exposure to Data Science or AI/ML workloads.
• Strong problem-solving skills and willingness to learn new technologies.

Nice to have

• Databricks certification or related training.
• Experience with monitoring and observability tools such as CloudWatch, Splunk, Datadog, or Grafana.
• Familiarity with MLflow, machine learning workflows, or MLOps concepts.
• Experience supporting enterprise data platforms or scientific computing environments.

Details

• Location: Hyderabad Tech Center, Telangana, India; candidates are expected to reside within commuting distance of the Hyderabad office.
• Hybrid working model: 3 days onsite, 2 days remote.
• No travel required.
• Visa sponsorship: No.
• Relocation: Domestic.