Anuj Singh
I am a Senior Cloud Data Engineer with 9+ years of industry experience delivering large-scale data engineering solutions across AWS, Azure, and GCP.
About
I am a Senior Cloud Data Engineer with 9+ years of industry experience delivering large-scale data engineering solutions across AWS, Azure, and GCP. I specialize in ETL/ELT pipelines, CDC processing, metadata-driven ingestion frameworks, data modeling, data warehousing, and real-time streaming architectures.
I design, build, and optimize production-grade data platforms, automate CI/CD pipelines, enforce data quality and security standards, and collaborate with cross-functional stakeholders in financial services, SaaS, and analytics-driven environments.
- 3 years' experience
- Joined January 2026
Industry experience
- Financial Services
- Software & Internet
- Professional Services
Work experience
Architected and delivered a reusable Python SDK for standardized ETL pipelines, reducing development time by 35% across multiple teams. Designed and implemented metadata-driven ETL pipelines using JSON configurations, supporting mappings, transformations, and data quality rules. Deployed cloud infrastructure and data pipelines across Dev, QA, and UAT using Terraform and Jenkins-based CI/CD pipelines. Implemented event-driven orchestration using AWS Step Functions, Glue, and Lambda, improving pipeline reliability and auditability. Built automated end-to-end testing frameworks simulating production workloads, reducing post-release defects by 30%.
Led development of an enterprise LLM-powered chatbot using OpenAI, LangChain, and LiteLLM, serving 500+ internal users. Implemented vector embeddings and semantic search, improving response relevance by 40%. Designed RESTful APIs with Flask, enabling secure project-level model configuration via Azure Key Vault. Integrated GPT-4 multimodal capabilities to generate structured metadata from documents and images.
Migrated large-scale banking datasets from on-prem systems to Azure Data Lake, reducing data processing latency by 30%.
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