Top Tech Jobs & Startup Jobs in Delhi

Develop scalable full-stack enterprise applications using Java, Spring Boot, microservices, REST APIs, and React or Angular. Design architectures, integrate databases and messaging systems, implement security, testing, CI/CD, Docker, Kubernetes, and cloud deployments. Apply agentic AI, LLM integrations, AI coding tools, prompt engineering, and tool calling to improve development workflows. Troubleshoot production issues, collaborate across technical teams, conduct code reviews, and mentor developers.

Designs and develops scalable Microsoft Fabric and Azure data platforms, including ETL/ELT pipelines, APIs, integrations, data warehouses, lakehouses, and analytical data models. The role supports Power BI reporting, Salesforce integration, data quality, monitoring, CI/CD, performance optimization, production troubleshooting, and collaboration with business, CRM, reporting, and engineering teams.

Lead end-to-end Salesforce CRM testing across implementations and transformation programs. Own test strategy, planning, SIT, UAT, regression, integration, functional, data migration, API, and release testing. Coordinate test teams, manage defects, validate integrations and migrated data, support business users through UAT, report quality metrics and risks, and provide go-live readiness assurance. Drive QA process improvements, automation, test coverage, and post-release validation.

Designs and leads enterprise business intelligence and analytics solutions using Power BI, Microsoft Fabric, Snowflake, Azure Data Factory, and modern data architectures. Responsibilities include developing semantic models, datasets, dashboards, data pipelines, data products, and scalable data platforms; implementing governance, quality, lineage, and metadata practices; enabling self-service analytics; optimizing performance; evaluating emerging tools such as Promethium; and advising stakeholders while mentoring data teams.

Design, build, and support production-grade AWS data pipelines that transform operational data into secure, high-quality, AI-ready datasets. Responsibilities include distributed data processing, Parquet curation, privacy-preserving transformations, orchestration, data quality monitoring, schema management, CI/CD, infrastructure as code, metadata and lineage management, troubleshooting, and reliable backfills.