Senior Data Engineer- Integration and Analytics
Infosys
The DNA unit is hiring data professionals specializing in ETL/data integration, database administration, and business intelligence. Candidates should have strong hands-on experience with enterprise data platforms and a solid understanding of data lifecycle management
OPEN REQUISITIONS
- Informatica Developer
Informatica PowerCenter – mapping, workflow development, and debugging Data pipeline development, transformation logic, and error handling Collaboration with data architects and business analysts
- Informatica Developer – PC/SQL/Unix
Informatica PowerCenter with SQL and Unix shell scripting Database query optimization and source-to-target mapping Unix/Linux environment – file handling, scheduling, scripting
- Informatica Administrator
Informatica platform administration – installation, upgrades, patching Repository management, domain configuration, and performance tuning User management, licensing, and monitoring of Informatica services
- Data Integration Engineer – IICS / Talend / Snowflake
IICS (Informatica Intelligent Cloud Services) – cloud-native data integration Talend – ETL pipeline design, job orchestration Snowflake – data loading, ELT patterns, performance optimization UNIX scripting for automation and scheduling
- Exadata DBA
Oracle Exadata – administration, patching, performance tuning RAC, ASM, Data Guard – high availability and disaster recovery Backup and recovery, capacity planning, query optimization
- BI Reporting Developer
MicroStrategy, Qliksense, Qlikview – dashboard and report development Power BI and Tableau – self-service analytics and enterprise reporting SQL – data modeling, query writing, report optimization Understanding of data warehouse and dimensional modeling concepts
Bachelor's degree in Computer Science, Information Systems, or equivalent 5+ years of hands-on experience in data/analytics domain Strong SQL skills across all roles Exposure to cloud data platforms preferred (AWS, Azure, GCP) Good understanding of data governance and data quality practices
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