Vetted Data Validation Professionals

“Analytics-focused candidate with experience building SQL/Python pipelines and dashboards for donor, campaign, and website performance reporting. They have worked with messy multi-source data, standardized metric definitions, and delivered automated reporting that reportedly reduced manual effort by about 80%.”

Atharv Sankpal

Screened

Mid-level Data Analyst specializing in financial and healthcare analytics

Baltimore, MD4y exp

“Analytics professional with experience at JPMorgan and Deloitte, focused on financial and risk data. They stand out for building scalable SQL/Python data pipelines, KPI and forecasting dashboards, and retention/cohort metrics that improved reporting reliability, forecast accuracy, and planning speed.”

“Healthcare analytics professional with TCS/Humana experience turning messy claims and eligibility data into reliable reporting assets using SQL and Python. They combine strong data engineering and analytics execution with stakeholder management, including automating monthly claims reporting from half a day to under 5 minutes and driving a provider outreach effort that reduced claim rejection rates by about 20%.”

“Analytics professional with healthcare experience at United Health Group, focused on turning messy claims and transaction data into reliable reporting assets. They combine SQL, Python, and Power BI to automate analysis, define operational KPIs, and build dashboards that improved stakeholder visibility and helped reduce processing time by about 22%.”

“Built end-to-end ML and GenAI systems at Northern Trust, including a production RAG-based document intelligence platform for financial reports and contracts. Stands out for combining strong MLOps execution with practical product judgment—improving forecast accuracy by 22%, document review accuracy by 38%, and cutting deployment time by 45% while keeping latency and reliability production-ready.”

“Backend engineer in airport operations who built a highly customizable BFF-based system connecting airport staff workflows to a baggage sortation engine. Their architecture cut per-airport customization from 100-150 engineering hours to 1-5 hours, improved long-running operation performance by 45%, and shipped in 4 months instead of 6. They also explored AI-assisted backend customization with human validation and test-based safeguards.”

Lawrence Prabhakar

Screened

Mid-level Full-Stack Java Developer specializing in FinTech

USA5y exp

“Engineer with a thoughtful, hands-on approach to AI-assisted software development, treating AI as a force multiplier for debugging, prototyping, and large-codebase work rather than a substitute for judgment. Particularly strong in multi-agent coding workflows, contract-driven development, and maintaining consistency across backend, frontend, and testing through shared schemas and OpenAPI-based coordination.”

“Backend engineer who shipped a Spring Boot transactions/points feature while using AI selectively for boilerplate and test ideas, keeping core system design decisions in human hands. Stands out for rigorous validation of AI output against database behavior, concurrency, idempotency, and failure scenarios, and has also built a lightweight planner -> code gen -> validator agent-style pipeline with step-level logging and retries.”

mahmoud abboud

Screened

Executive project management leader specializing in core banking transformation

Houston, TX20y exp

“Senior implementation consultant in fintech focused on banking and credit union clients, with end-to-end ownership of Jack Henry LoanVantage implementations. Stands out for hands-on work across discovery, configuration, data migration, UAT, training, and go-live, including managing compliance-sensitive legacy migrations and complex operational cutovers.”

Sai Naveen Reddy Reganti

Screened

Mid-level Full-Stack Engineer specializing in cloud-native FinTech and Healthcare systems

null4y exp

“Full-stack engineer working on customer-facing utility and banking platforms, with hands-on experience across React/React Native, Java/Spring Boot, Python/Django, AWS, and SQL performance tuning. Stands out for owning production systems end to end, improving CI/CD and deployment reliability, and delivering a measurable database optimization that cut CPU utilization from about 80% to 40%.”

“Built and deployed a production LLM-powered RAG assistant for healthcare teams (care managers/support) to answer questions from clinical and policy documentation, emphasizing trustworthiness via improved retrieval, reranking, and strict grounding prompts to reduce hallucinations. Also has hands-on orchestration experience with Apache Airflow for end-to-end ETL/ML workflows and applies rigorous testing/metrics (hallucination rate, tool-call accuracy, latency, cost) to ensure reliable AI agent behavior.”

“Full-stack engineer with financial services experience (Lincoln Financial) who owned a customer-facing financial portal end-to-end using TypeScript/React and Node/Express. Has hands-on microservices and RabbitMQ event-driven workflows, addressing scale issues like retries/duplicates with idempotency and traceable logging, and built an internal real-time ops/support dashboard to improve monitoring and incident response.”

“Built production LLM/RAG platforms for financial services to enable natural-language Q&A over large policy/compliance document sets stored in Snowflake and SharePoint. Strong in MLOps and orchestration (Airflow, ADF, Step Functions, MLflow) and in solving real production issues like stale embeddings and model performance, including an incremental Snowflake Streams sync that cut processing time from hours to minutes.”

“Backend/platform engineer with hands-on ownership of high-stakes data migrations in regulated domains (core banking and insurance), including a Python ETL framework that migrated 100,000+ customer records within a cutover window. Strong DevOps/GitOps background deploying Python and Spring Boot microservices to Kubernetes with Helm and ArgoCD, plus real-time Kafka transaction streaming for fraud/analytics with reliability patterns (DLQs, retries, partition tuning).”

“Backend Python engineer who owned an AI-driven healthcare staffing matching service, rebuilding the model inference/data pipeline to eliminate blocking bottlenecks and cutting API latency by ~33%. Experienced running Python services on Kubernetes with GitOps/ArgoCD, and has executed a cloud-to-on-prem rollout under tight resource and tooling constraints while also building event-driven streaming updates via a message broker.”

“ML/NLP practitioner focused on financial-services document intelligence and compliance workflows—built an end-to-end pipeline to classify documents and extract financial entities from loan applications, emails, and statements stored in S3/internal databases. Strong in entity resolution/record linkage and in productionizing pipelines with GitHub Actions CI/CD, testing, data validation, and Docker, plus semantic search using OpenAI embeddings and a vector database.”

“Data/ML practitioner with experience at UnitedHealth Group building a fraud claims detection solution combining structured claims data and unstructured notes, validated with compliance stakeholders to improve actionable accuracy. Also applied embeddings, vector databases, and fine-tuned language models in a Bank of America capstone to detect threats/anomalies in financial documents, with production-minded Python ETL workflows using Airflow.”

“QA automation engineer who owned an end-to-end test suite for a financial payments application, building cross-layer E2E coverage (UI/API/DB) and integrating it into CI with smoke-on-commit and nightly regression. Caught high-impact issues including duplicate payments caused by missing idempotency in backend retry logic and an RBAC authorization gap, and has hands-on experience stabilizing flaky Cypress tests via network-call synchronization.”

“Backend engineer who built an AI-driven "Smart Feedback Analyzer" API (Flask → FastAPI) that processes user feedback with NLP (Hugging Face + OpenAI) and returns structured insights. Demonstrates strong production-minded architecture: stateless services, Cloud Run + Docker deployment, Redis/Celery background processing, and Postgres/SQLAlchemy performance tuning (EXPLAIN ANALYZE, indexing, N+1 fixes), plus multi-tenant data isolation via JWT/API-key derived tenant IDs.”

Rupak Chand

Screened

Junior ML Data Associate specializing in AI training data and LLM prompt evaluation

Connecticut2y exp

“Applied ML/embodied AI practitioner who built an on-device gesture-control system for smart-home lights using Raspberry Pi + camera, focusing on privacy-preserving real-time inference and hardware-constrained optimization (async pipeline + TF Lite INT8). Also made a high-impact architecture decision for an ML content evaluation/QA pipeline processing millions of annotated text samples weekly, reducing batch runtime from ~6 hours to ~40 minutes while lowering compute cost.”

Abhishek Soni

Screened

Mid-level Full-Stack Developer specializing in React and scalable web applications

Mumbai, India3y exp

“Backend/data engineer with hands-on production experience across FastAPI microservices and AWS data platforms. Has delivered serverless and Glue/EMR-based ETL pipelines with strong observability (Prometheus/Grafana/Sentry, CloudWatch/SNS), schema-evolution resilience, and measurable SQL performance wins (5 min to <30 sec). Open to onsite meetings in the Bethesda, MD area and flexible on remote arrangements.”

Prasanth Sai

Screened

Mid-level Data Engineer specializing in cloud lakehouse/warehouse pipelines

4y exp

“Data engineer with HCA Healthcare experience building and operating end-to-end AWS-based pipelines for clinical and operational reporting (50–100 GB/day), serving curated data into Redshift/Snowflake for Power BI/Tableau. Emphasizes production reliability (Airflow SLAs/retries/alerting, logging/observability) and strong data quality controls (reconciliations, schema/null/duplicate checks), and has shipped versioned REST APIs to expose warehouse data to downstream systems.”

“Accenture data engineer who greenfielded a supply-chain lakehouse platform, building an end-to-end medallion/Delta pipeline ingesting ~1.4TB/day from 17+ ERP/WMS/TMS/shipment sources. Delivered Gold datasets to Redshift/Synapse/Databricks SQL powering Power BI/Tableau with a 99.5% SLA, while cutting runtime 30% and cloud costs 16% through Spark/Delta optimizations and robust data quality controls.”