Senior Specialist - Data Engineering

LTM • Design & Creative • Information Technology • AWS Glue • 4 days ago
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Salary
≈ $21k – $47k per year (Estimated)
Location
In office (Bengaluru)
Seniority
Senior

Confirmed on the employer's own hiring board on Oct 27, 2026. First seen by Alion on Oct 22, 2026. LTM scores B on the Alion truth index.

Overview
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Profile match

LTM

LTM (formerly LTIMindtree) is a prominent global technology consulting and digital solutions enterprise that delivers comprehensive IT, cloud, and business transformation services to multinational corporations. Headquartered in Mumbai, India, the company operates an extensive international delivery network specializing in artificial intelligence, data engineering, and enterprise systems integration. As a subsidiary of the industrial conglomerate Larsen & Toubro, it partners with leading organizations across sectors such as banking, healthcare, and manufacturing to drive operational efficiency and digital modernization.
ltm.com • HQ: Mumbai, India • Information Technology • Design & Creative • Branding • Creative Agencies • Media & Entertainment • Enterprise Apps Implementation Partners • IT Outsourcing & Dedicated Teams • IT Consulting & Digital Transformation • Est. 1996
Tech stack of LTM

Role: AWS Data Engineer

Responsibilities:

Design develop and optimize largescale data processing pipelines using Apache Spark

Build and maintain ETLELT workflows using AWS Glue Amazon S3 and Amazon Athena

Orchestrate endtoend data pipelines and processing workflows using AWS Step Functions

Develop ingestion frameworks to handle multivendor data schemas including Ericsson and Oracle datasets

Standardize transform map and integrate heterogeneous vendor data into common data models

Implement data validation reconciliation error handling and dataquality controls across ingestion pipelines

Optimize Spark jobs Glue workloads storage structures queries and underlying datastore performance

Monitor pipeline execution troubleshoot failures perform rootcause analysis RCA and improve processing reliability

Support schema evolution metadata management partitioning and performance optimization for large datasets

Collaborate with architecture platform analytics and downstream application teams to ensure scalable and reliable data delivery

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