Jr Databricks SQL Engineer (Pharma/Life Sciences background)

Job Description

• • Years of Experience: 3 - 4 Yrs ( PHARMA MANDATORY )
  
• • Role Type: Fixed Term Contract (6 Months)
  
• • Payroll Organization: Talpro
  
• • CTC Offered: ₹100,000 per month (Maximum Rate)
  
• • Shift Timing: Day shift IST, from 12pm
  
• • Notice Period: Immediate Joiners Only
  
• • Work Location: Mumbai / Pune / Bangalore / Chennai / Ahmedabad / Noida (Chennai Most Preferred)
  
• • Work Mode: Hybrid (3 Days from Office Weekly)
  
• Mandatory Skills:
  
• Databricks 
  
• SQL
  
• PySpark
  
• Pharma or Life Sciences domain experience
  
  

Good to Have Skills:

• Delta Lake
  
• Data Lakehouse Architecture
  
• Unity Catalog or similar data governance tools
  
• SQL development & performance tuning with large datasets
  
• Git for version control
  
• CI/CD for data pipelines (Databricks Repos, Terraform, dbx)
  
• Azure Databricks or Databricks on AWS
  
• Python for data manipulation
  
• Structured streaming in Spark
  
• Azure Purview or equivalent governance tools
  
• Automation experience with Terraform/dbx
  
• Databricks Notebooks
  
• Data documentation and dictionary practices
  
  

Role Overview / Job Summary:

We are looking for an experienced Databricks SQL Engineer with a Pharma or Life Sciences background to join our offshore Data Engineering team. This role focuses on building efficient, scalable SQL-based data models and pipelines using Databricks SQL, Spark SQL, and Delta Lake. The ideal candidate will play a key role in transforming raw data into valuable analytical insights, enabling critical decision-making across pharma-related business functions.

• Key Responsibilities / Job Responsibilities:
  
  
• • Design and optimize SQL queries and data models in Databricks for large-scale datasets
  
• • Develop and maintain robust ETL/ELT pipelines using Databricks workflows
  
• • Implement Delta Lake and Unity Catalog for secure and governed data assets
  
• • Ensure data quality via validation, testing, and monitoring mechanisms
  
• • Optimize performance and cost for the data lakehouse environment
  
• • Collaborate with stakeholders to support analytics and business needs
  
• • Deploy notebooks and SQL workflows using CI/CD best practices
  
• • Document pipelines, queries, and data models to foster self-service