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Salary
≈ $22k – $46k per year (Estimated)
Location
In office (Kolkata)
Seniority
Senior · 5+ years exp

Confirmed on the employer's own hiring board on Oct 16, 2026. First seen by Alion on May 11, 2026. Ltm scores B on the Alion truth index.

Overview
Company
Impact
Profile match

Ltm

LTIMindtree is a worldwide technology consulting and digital solutions company that empowers businesses in a variety of sectors to get to the future faster by combining engineering DNA and tech expertise.

Job Summary

Seeking a Specialist with 5 to 7 years of experience in designing and implementing scalable data engineering solutions using Azure SQL and Databricks within the Azure Databricks skill cluster family

Job Description

Design develop and maintain scalable and efficient data pipelines and ETL processes using Azure SQL and Databricks Collaborate with source data application teams and product owners to understand data requirements and deliver robust analytics solutions Utilize Azure SQL and Databricks for data ingestion transformation storage and processing Implement data migration and transformation workflows leveraging Azure cloud services Ensure data quality and optimize performance of data pipelines Support continuous integration and continuous deployment CICD pipelines for data engineering projects Apply best practices for data governance privacy and security within Azure environments Stay updated with emerging Azure and data engineering technologies and recommend improvements to existing solutions

Roles and Responsibilities

Lead the design and implementation of batch and streaming data pipelines using Azure SQL and Databricks Provide technical leadership and mentorship to team members on Azure SQL and Databricks best practices Collaborate with DevOps teams to manage deployment monitoring and support of data engineering solutions Troubleshoot and resolve data pipeline issues ensuring high availability and reliability Participate in architectural discussions and contribute to the development of coding standards and security guidelines Engage with crossfunctional teams to align data engineering efforts with business objectives Drive automation of data workflows and improve operational efficiency Support production environments with timely incident response and root cause analysis

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