Optimize Spark PySpark and SQL workloads to improve performance reliability cost efficiency and scalability
Support production data pipelines through monitoring troubleshooting incident resolution and continuous improvement
Implement data engineering standards CICD practices automated deployment processes unit testing and code quality expectations
Partner with BI Product Engineering and clientfacing teams to translate business and reporting requirements into scalable data solutions
Document technical solutions data flows pipeline logic and operational processes to support knowledge sharing and longterm maintainability
Responsibilities
Design build maintain and optimize data pipelines using Python SQL PySpark Databricks and AWSbased data services
Develop ETLELT processes that support data warehousing analytics reporting and business intelligence use cases
Build and optimize Spark jobs with a focus on performance scalability reliability and efficient resource utilization
Design and implement data models for structured semistructured and NoSQL data where applicable
Implement CICD practices automated deployments unit tests and code quality standards for data engineering workflows
Monitor troubleshoot and support production data pipelines resolving issues and recommending improvements
Collaborate with BI Analysts Product Engineering Data and clientfacing teams to understand requirements and support reporting needs
Work Experience Requirements
Minimum of 35 years of experience in Data Engineering preferably working with AWSbased cloud data platforms
Handson experience building maintaining and supporting cloudbased data pipelines
Strong knowledge of PySpark preferably on the Databricks platform
Handson experience with Databricks
Strong proficiency in SQL including query optimization
Robust proficiency in Python
Strong knowledge of data modeling data warehousing ETLELT and analytics concepts
Experience with CICD practices automated deployment processes unit testing and code quality standards
Experience troubleshooting monitoring and supporting production data pipelines
Experience documenting technical solutions data flows and pipeline logic
Skills:
Mandatory Skills : AWS ML, Data engineering, ETL & integration, Databricks, Prompt Engineering & RAG, Python
Good to Have Skills : AWS AI Services, AWS Bedrock, AWS S3, AWS SageMaker, Data Architecture, Databricks Python
About Company:
LTM is an AI-centric global technology services company and the Business Creativity partner to the world’s largest and most disruptive enterprises. We bring human insights and intelligent systems together to help clients create greater value at the intersection of technology and domain expertise. Our capabilities span integrated operations, transformation, and business AI — enabling new ways of working, new productivity paradigms, and new roads to value. Together with over 87,000 employees across 40 countries and our global network of partners, LTM — a Larsen & Toubro company — owns business outcomes for our clients, helping them not just outperform the market, but to Outcreate it. Please also note that neither LTM nor any of its authorized recruitment agencies/partners charge any candidate registration fee or any other fees from talent (candidates) towards appearing for an interview or securing employment/internship. Candidates shall be solely responsible for verifying the credentials of any agency/consultant that claims to be working with LTM for recruitment. Please note that anyone who relies on the representations made by fraudulent employment agencies does so at their own risk, and LTM disclaims any liability in case of loss or damage suffered as a consequence of the same. Recruitment Fraud Alert - https://www.ltm.com/careers/recruitment-fraud-alert