Vetted ETL Pipelines Professionals

Deeksha V

Screened ReferencesStrong rec.

Mid-Level Software Engineer specializing in cloud data platforms and FinTech payments

Atlanta, GA4y exp

“Backend engineer focused on financial systems, having evolved payments and reconciliation platforms using Python/FastAPI and PostgreSQL with an emphasis on idempotency, validation, and consistency. Has led monolith-to-services migrations using feature flags and shadow traffic, and implements defense-in-depth security (OAuth2/JWT plus DB-level row security) for multi-tenant environments.”

“Backend/data engineer with hands-on production experience building FastAPI Python APIs and AWS-native platforms (Lambda/API Gateway, SQS, ECS Fargate) with Terraform + GitHub Actions CI/CD and strong reliability practices (JWT/RBAC, retries/timeouts, structured errors/logging). Also built AWS Glue ETL pipelines (S3/RDS to curated S3/Athena) with schema evolution and data quality controls, modernized legacy processing via parallel-run validation and phased cutovers, and has demonstrated SQL tuning impact (seconds to <200ms) plus incident ownership for batch pipeline SLAs.”

Emmanuel Ngalima

Screened ReferencesStrong rec.

Mid-Level Software Engineer specializing in geospatial AI and cloud security automation

San Ramon, CA5y exp

“Cloud engineer and cloud OS SME (Chevron) who productionized large-scale security remediation—using Tanium and Ansible to address CIS benchmark noncompliance across 5,000+ servers with robust logging and RCA handoffs. Also drives adoption of a geospatial AI refinery inspection product by consolidating siloed imagery into an enterprise geospatial database, and presents internally on agentic/LLM tooling (LangChain/LangGraph, LangSmith observability).”

“Built and deployed an AI Copilot for Healthful Telehealth that helps dietitians generate personalized meal plans using patient data and real-time clinical context. Stands out for owning the full lifecycle—from workflow discovery and ETL/RAG architecture to production incident response and post-launch stabilization—while delivering roughly 30% gains in retrieval accuracy and latency.”

“ML/AI engineer with hands-on experience shipping production computer vision and GenAI systems, including a fabric defect detection platform that combined vision models with agentic LLM workflows to reach 89% human-inspector agreement at 200 ms latency. Also built a RAG-based code QA tool for developers and emphasizes production monitoring, evaluation, caching, and reusable Python service design.”

“Built a production LLM-powered fraud detection platform at Wells Fargo, combining OpenAI/Hugging Face models with RAG-based explanations to make flagged transactions interpretable for risk and compliance teams. Delivered low-latency, real-time inference at high scale on AWS (SageMaker + EKS), with strong observability and security controls, reducing manual reviews and false positives in a regulated environment.”

“AI/ML Engineer (TCS) who built and deployed a production LLM-powered audit transaction validation service to reduce manual review of unstructured transaction records and comments. Implemented a LangChain/Python pipeline for extraction/normalization and discrepancy detection, with strong production reliability practices (decision logging, dashboards, labeled eval sets) and a human-in-the-loop auditor feedback loop to improve precision/recall under strict data-sensitivity and near-real-time constraints.”