Specialist, Databricks Platform Engineer

Merck; Hyderabad; Hybrid; INR 1,200,000 - 2,300,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

Merck Hyderabad Tech Center seeks a hands-on Databricks Platform Engineer to support administration, operation and continuous improvement of enterprise Databricks workloads. You will partner with platform teams, Data Engineers and Data Scientists to maintain reliable jobs, pipelines, compute, and access controls while driving observability and reliability.

You will work in a hybrid environment with 3 days on site and 2 days remote, contributing to platform upgrades, monitoring dashboards and

Qualifications

• Experience with Databricks administration and cluster management.
• Solid understanding of data engineering concepts and pipelines.
• Ability to monitor, diagnose, and resolve platform issues.
• Familiarity with monitoring tools and incident response.

Responsibilities

• Administer and operate Databricks environments, including workspaces and clusters.
• Manage jobs, workflows, and Unity Catalog permissions.
• Monitor health and performance; investigate failures via logs and metrics.
• Collaborate with Data Engineers and Scientists to optimize workloads.
• Assist with platform upgrades and operational improvements.
• Develop and maintain runbooks and troubleshooting guides.

Skills

Databricks administration

Data engineering concepts

Platform observability

Troubleshooting

Tools

CloudWatch

Splunk

Datadog

Grafana

Job description

Job Description

The Opportunity

Join a global biopharma company with a 130-year legacy and mission to achieve new milestones in healthcare. Be part of technology driven, data-led organization supporting a diversified portfolio of medicines, vaccines and animal health products. Work alongside passionate teams that use data, analytics and insights to drive decisions, and tackle some of the world's greatest health threats.

Our Technology Canters are globally distributed hubs that enable our digital transformation and business outcomes across IT. They bring together diverse teams to collaborate, share best practices, and deliver solutions that save and improve lives.

This role is based at our Hyderabad Tech Center and follows a hybrid working model (3 days onsite, 2 days remote). Candidates are expected to reside within commuting distance of the Hyderabad office.

Role Overview

We are seeking a hands-on Databricks Platform Engineer to support the administration, operation, monitoring, and continuous improvement of our enterprise Databricks platform.

The ideal candidate will have experience with Databricks administration, data engineering concepts, platform observability, and technical troubleshooting. This individual will work closely with platform teams, Data Engineers, Data Scientists, and other technical users to maintain a reliable platform and help diagnose issues related to jobs, pipelines, compute, access, performance, and user environments.

The role is well suited for someone who enjoys solving technical problems, working directly with users, learning new technologies, and experimenting with new Databricks capabilities.

What will you do in this role

Databricks Administration & Platform Operations

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.

Data Engineering & Workload Support

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.

Platform Observability & Reliability

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
• 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.

User Support & Troubleshooting

Work directly with Data Engineers, Data Scientists, analysts, and other Databricks users to understand and troubleshoot technical issues.

Support problems involving notebooks, jobs, libraries, compute, permissions, data access, Python/SQL environments, and Spark workloads.

Guide users through appropriate troubleshooting steps and escal…

Data Science & AI Enablement

Support Databricks environments used by Data Science and analytics teams.

Understand common Data Science workflows involving Python, SQL, notebooks, Spark, MLflow, and machine learning libraries.

Assist Data Scientists with environment, library, compute, permissions, and data access issues.

Support the configuration and operation of compute environments used for Data Science and ML workloads.

Develop w