Specialist, Databricks Platform Engineer
MSD (Merck & Co., Inc.) (company site, opens in a new tab)3–5 yrsHyderabadHybridDirect career pageEst. 12–20 LPA
Posted 13h ago
Reach people at MSD (Merck & Co., Inc.)
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at MSD (Merck & Co., Inc.) you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Administer, operate, monitor, and continuously improve enterprise Databricks platform including workspaces, clusters, jobs, SQL Warehouses, and user access.
- Diagnose and troubleshoot platform and workload issues involving compute resources, jobs, pipelines, permissions, data processing, and performance.
- Develop monitoring dashboards, support user troubleshooting, assist Data Engineers and Data Scientists, and explore new Databricks capabilities for platform improvements.
Minimum Requirements
- Bachelor’s or master’s degree in computer science, engineering, data science, or related field.
- 3-5 years experience in Data Engineering, Data Platform Administration, or Cloud Data Platforms with 1-2 years hands-on Databricks administration.
- Experience with Unity Catalog, Delta Lake, Databricks Jobs/Workflows, SQL Warehouses, Python, SQL, Apache Spark/PySpark, and AWS cloud environment.
- Based in or within commuting distance of Hyderabad; hybrid work model 3 days onsite, 2 days remote. Work Experience Required: 3-5 years in relevant fields. Notice Period: Not explicitly mentioned.
Ideal Candidate Profile
- Experienced Databricks Platform Engineer familiar with platform operations and technical troubleshooting in a cloud environment, preferably AWS.
- Strong technical skills in data engineering, Spark ecosystem, troubleshooting workloads, and collaborating directly with Data Engineers and Data Scientists.
- Focused on operational reliability, user support, observability, and continuous platform improvements through experimentation and automation.