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| Job Location | Bengaluru/ Bangalore (Karnataka) |
| Education | Any Graduate |
| Salary | 30.20 - 35.00 Lakhs |
| Industry | IT-Software |
| Functional Area | IT Software : Software Products & Services |
| EmploymentType | Full-time |
Job Description Years of Experience: 5 years ( PHARMA MANDATORY ) Role Type: Fixed Term Contract (6 Months) Payroll Organization: Talpro CTC Offered: 1,25,000 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