Vetted Data Validation Professionals

“Analytics professional with hands-on experience turning messy SAP enterprise data into trusted reporting layers and building end-to-end Python/Tableau analytics products. Stands out for combining technical rigor with business alignment—improving report accuracy by 30%, cutting refresh times by 25%, and independently delivering a CLV segmentation project across 96,000 customers that informed retention strategy.”

“Backend/data engineer in healthcare who built an AWS-based clinical analytics platform from scratch (DynamoDB/S3/Airflow/dbt) with sub-second clinician query goals, 99.9% uptime, and HIPAA-grade controls (KMS encryption, IAM RBAC, audit trails). Also modernized ML delivery by replacing a manual 4-hour deployment with a 30-minute Docker/GitHub Actions CI/CD pipeline using parallel runs, parity testing, and rollback, and caught critical EHR data edge cases (date formats/timezones) that could have impacted patient care.”

“Backend engineer working on Plumas Bank’s digital modernization, building a FastAPI-based loan origination/processing system with OAuth2/JWT security, AWS Lambda-driven PDF document generation to S3, and MongoDB integration. Has led a legacy workflow migration to a new microservice using dual-write/dual-read and monitoring, and emphasizes multi-tenant isolation via layered API controls plus row-level security.”

“Backend/data engineer who built a production-ready multi-agent financial intelligence system (Mycroft) that orchestrates specialized AI agents to analyze real-time market data using FastAPI and Pinecone vector search. Brings strong security/reliability instincts (rate limiting, JWT/OAuth2, retries/backoff, health checks) and has caught high-impact data integrity issues in financial migrations (timezone normalization across global legacy systems).”

“Built a production GPT-4/LangChain/Pinecone RAG “AI Copilot” at Northern Trust to automate financial report generation and analyst Q&A over internal structured (SQL warehouse) and unstructured policy data. Focused on real-world production challenges—grounding and latency—achieving major speed gains (seconds to milliseconds) via MiniLM embedding optimization and Redis caching, and implemented rigorous testing/evaluation with MLflow-backed metrics while aligning compliance and finance stakeholders for deployment.”

“Built and operated an end-to-end customer-facing "Record Platform" web product as both engineer and primary user, focusing on reliability and correctness in core flows like search and checkout. Implemented a TypeScript/React frontend with a multi-service backend and Kafka-based event-driven architecture, and created internal tooling to automate risky ops like Kubernetes TLS certificate rotation with k6 load/chaos testing (including HTTP/2 and HTTP/3 validation).”

“Built and shipped “LabMate,” a production AI assistant specialized in laboratory hardware, using a weighted multi-source RAG pipeline with reranking and reasoning-focused query decomposition to handle complex user questions. Deployed on a local GPU cluster with vLLM and NVIDIA MPS (plus OCR/VLM components), and established evaluation using synthetic + public reasoning datasets while collaborating weekly with non-technical admins to align requirements and resource constraints.”

“Backend/data platform engineer who owned a FastAPI + Kafka microservice in Verizon’s billing pipeline, handling high-volume usage ingestion/validation/enrichment with strong observability and CI/CD on AWS EKS. Demonstrated measurable performance gains (latency down to ~120–150ms; Kafka throughput +30–40%; DB CPU -25%) and led an on-prem ETL-to-AWS migration using Terraform, parallel validation, and phased cutover with zero downtime.”

“QA automation engineer with strong Cypress/JavaScript experience in healthcare claims and eligibility systems, owning end-to-end regression suites that combine UI, API, and SQL/database validations. Known for catching subtle pricing/benefit calculation defects (copay/deductible/accumulator issues) before release, stabilizing flaky CI tests via API synchronization, and shaping requirements early to improve testability and reduce downstream rework.”

“Backend/platform engineer with Optum experience owning a production FHIR Member Access API aligned to CMS interoperability requirements. Built and scaled Spring Boot/HAPI FHIR microservices on AWS (Docker/Kubernetes) with zero-downtime CI/CD, and operated them with strong observability (Dynatrace, logs/metrics, alerting) and incident response. Also implemented a Kafka-based FHIR bulk data pipeline with schema versioning, idempotent processing, and reliable backfills/replays.”

“Built a production AI legal search platform that uses a retrieval-first, source-grounded LLM pipeline with confidence-based fallbacks and structured, traceable outputs to reduce hallucinations and improve trust. Also has experience at Discover Excellence building real-time analytics and identity stitching systems, emphasizing conservative data validation, idempotent processing, and fault-tolerant queue-based workflows.”

prashansa chaudhary

Screened

Mid-Level Full-Stack Software Engineer specializing in cloud-native data platforms and AI apps

Redwood City, CA4y exp

“Software engineer who has owned customer-facing/internal platforms end-to-end, emphasizing fast iteration through small releases backed by monitoring and rollback safety. Built SurveyAI with reusable React/TypeScript components and a stateless Node.js REST backend with clear API contracts/validation, and created an internal Airflow + AWS Lambda automation tool integrated with Slack alerts to reduce manual work and improve response time.”

Damaris Villa González

Screened

Entry Mechatronics Engineering student specializing in robotics and embedded control

Guadalajara, México1y exp

“Hands-on builder with projects spanning IoT/backend data systems and robotics: created a flow meter pipeline that sends readings over WiFi to a database and web server with real-time averaging and easy data download. Also diagnosed a hard-to-catch AGV/AMR reliability issue during long autonomous runs (battery voltage drop causing sensor failures) and mitigated it with voltage monitoring and a buffer circuit; familiar with Python/FastAPI API best practices (Pydantic, async, OAuth2/JWT).”

Veena Vyshnavi Garre

Screened

Senior Full-Stack Software Engineer specializing in cloud-native systems and AI/ML

Hyderabad, India7y exp

“Backend engineer who significantly evolved an internal Resource Manager platform, moving from a monolith to microservices and improving onboarding speed while reducing integration errors. Has hands-on experience building reliable and secure Python/FastAPI APIs (Pydantic schemas, circuit breakers, caching, metrics/alerts) and leading zero-downtime migrations with strong data integrity patterns (dual writes, idempotency, reconciliation checks).”

Haritha Kuraparthi

Screened

Mid-level Full-Stack Developer specializing in cloud data engineering and analytics

West Haven, CT4y exp

“Software developer with hands-on experience owning customer-facing work end-to-end (requirements, implementation, testing, and feedback-driven iteration) using Python and React.js. Also described remodeling an internal legacy page/tool to improve performance and accuracy, and has exposure to microservices and RabbitMQ plus ETL-based system work.”

Sai Krishna Mallikanti

Screened

Mid-level AI & Data Scientist specializing in LLMs, RAG, and healthcare NLP

TN4y exp

“Built a production LLM/RAG solution for healthcare operations teams to query large policy and care-guideline repositories in natural language. Improved domain alignment using vector retrieval plus parameter-efficient fine-tuning and prompt optimization, validated through internal user testing and metrics, cutting manual lookup time by ~40%. Also has hands-on experience orchestrating automated ML pipelines with Apache Airflow.”

Alankrit Srivastava

Screened

Intern Data Engineer specializing in Snowflake pipelines and AI/ML analytics

Houston, TX3y exp

“Built and operated an end-to-end TypeScript/Node AI agent platform for high-volume financial data that generates explainable investment signals and automates execution via resilient Playwright browser automation. Uses Postgres + pgvector/Prisma for RAG retrieval, Redis for async orchestration, Zod-based boundary validation as a circuit breaker, and OpenTelemetry for tracing/latency monitoring; also designed a TypeScript SDK with semver, scoped bearer-token auth, CLI key rotation, and interactive Swagger docs.”

“Data/ML engineer with end-to-end ownership of donor-data deployments for a university foundation, delivering major performance and data-quality gains (500K+ records; 24h to 6h processing; duplicates 5% to 1%). Has put an LLM-assisted enrichment workflow into production with retrieval-grounded business rules, versioned outputs for traceability, and strong operational rigor around validation, logging, and CI/CD.”

“Analytics professional with hands-on experience building SQL and Python workflows across SAP, Oracle, and internal operational systems, processing roughly 5 million records per month. They combine strong data quality rigor with stakeholder-friendly Power BI reporting, and cite a concrete impact of cutting reporting turnaround time from four days to two while surfacing cost anomalies for business teams.”

“Analytics candidate with hands-on experience improving enterprise reporting and operational decision-making at Reliance and Wendy’s. They combine SQL optimization, Python automation, sentiment analysis, and dashboarding to deliver measurable impact, including cutting report runtimes from 3 minutes to 1 minute, improving model accuracy from 70% to 80%, and reducing supplier past dues by 30%.”

“Backend/data engineer with production experience building Python FastAPI services and AWS-native data pipelines. Has delivered containerized and serverless workloads (ECS/EKS/Lambda) with Terraform-based IaC, strong reliability patterns (JWT/RBAC, retries/circuit breakers, observability), and AWS Glue ETL into S3/Redshift. Demonstrated measurable SQL performance wins (40–50s to <4s) and owned real pipeline incidents through detection, mitigation, and prevention.”

“Backend/data engineer who builds Python (FastAPI) data-processing API services for internal analytics/reporting, emphasizing modular architecture, async performance tuning, and reliability patterns (health checks, retries, observability). Also migrated legacy on-prem ETL pipelines to Azure using ADF/Data Lake/Functions and implemented a near-real-time ingestion flow with Event Hubs plus watermarking to handle late events and deduplication.”

“QA automation engineer focused on end-to-end quality for a CMS lien registration workflow, owning a Playwright-based regression suite covering high-risk paths (creation, amendments, cancellation, batch file validation). Demonstrated impact by catching a UI change that bypassed required-field validation pre-release, stabilizing flaky CI tests using network-response signals, and driving clearer acceptance criteria and observability improvements (request IDs in logs) through cross-functional collaboration.”