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Posted 13 May 2025

Jr Databricks SQL Engineer (Pharma/Life Sciences background)

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Employment
Full time
Workplace
Hybrid
Posted
13 May 2025

About the role

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


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