Specialist, Databricks Platform Engineer

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 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 rather than simply routing support tickets. 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 working knowledge of emerging AI/ML capabilities available within the Databricks ecosystem. Technology Exploration & Continuous Improvement Stay informed about new Databricks features, runtimes, administrative capabilities, and platform improvements. Experiment with relevant new capabilities in development or test environments. Participate in small proofs-of-concept to understand how new features could improve reliability, observability, cost, security, or user experience. 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 should you have 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. Secondary skills (nice to have)