Vetted Data Validation Professionals

“Data/analytics engineer who owned Angi’s service-request funnel event pipeline end-to-end, routing events server-side to bypass ad blockers and recovering ~15% lost tracking at millions of events/day. Built Snowflake/dbt reporting tables powering Looker dashboards, with strong emphasis on validation, monitoring/alerting, and safe schema evolution. Also shipped a reusable flow state management backend service with TTL storage, CI/CD, and developer-friendly APIs.”

“Data/QA-focused professional with experience at Lonza and Jio Platforms, owning end-to-end data reliability work for QC forecasting (SAP/LIMS) and driving root-cause/corrective actions for recurring environment configuration issues. Strong in SQL-based data standardization/validation, requirements tightening for API consistency, and coordinating multi-workstream delivery using Jira/Azure DevOps.”

“Data engineer with healthcare domain experience who has owned end-to-end pipelines and APIs at UnitedHealth Group, processing ~8M records per batch. Strong focus on data quality (multi-layer validation), reliability (monitoring/logging, retries/idempotency), and performance (Spark/SQL tuning, caching), with experience standing up early-stage systems using Python, Docker, and CI/CD.”

“Data engineer focused on reliable, production-grade pipelines and data services: has owned end-to-end ingestion-to-serving workflows processing millions of records/day, using Airflow, Python/SQL, and PySpark. Demonstrates strong operational rigor (monitoring, retries, idempotency, backfills) and measurable outcomes (98% stability, ~30% faster processing), plus experience exposing curated warehouse data via versioned REST APIs.”

“Data engineer in a banking context who has owned end-to-end Azure lakehouse pipelines ingesting financial/vendor data from APIs, Azure SQL, and flat files into Databricks/Delta (bronze-silver-gold). Emphasizes production reliability via schema-drift validation, data quality controls, monitoring/alerting, retries/checkpointing, and Spark/Delta performance tuning, with outputs served to BI/reporting teams (e.g., Tableau).”

Siddhanth Erramaraju

Screened

Junior Full-Stack Developer specializing in MERN and AWS

Birmingham, AL3y exp

“Backend engineer focused on Python/Flask APIs and cloud-native delivery: builds stateless services with JWT auth, validation, and scalable deployment on Kubernetes using a GitOps workflow (ArgoCD-style) with easy rollbacks. Has also implemented Kafka-based real-time event pipelines and supported phased hybrid cloud/on-prem migrations with parallel runs and controlled cutovers.”

Chandan Chalumuri

Screened

Mid-level Data Scientist specializing in ML, NLP, and Generative AI

Tempe, AZ4y exp

“Data engineering / ML practitioner with experience at MetLife building transformer-based sentiment analysis over large unstructured datasets and productionizing pipelines with Airflow/PySpark/Hadoop (reported 52% efficiency gain). Also implemented embedding-based semantic search using Pinecone/Weaviate to improve retrieval relevance and enable RAG for customer support and document matching use cases.”

vineetha Pulipati

Screened

Mid-level Software Engineer specializing in backend microservices and cloud data pipelines

MO, USA4y exp

“Backend engineer with Morgan Stanley experience building and owning an end-to-end Python FastAPI microservice for high-volume market data used by trading and risk systems. Strong in performance tuning and reliability (PySpark, Redis caching, async APIs), real-time streaming with Kafka, and production operations (Docker/Kubernetes, GitOps-style CI/CD, monitoring). Has led cloud/on-prem migration work across AWS and Azure, including fixing Azure Synapse performance issues via query and pipeline redesign.”

Amrita Mukherjee

Screened

Senior Talent Acquisition Manager specializing in executive search and account management

East Brunswick, NJ16y exp

“Staffing leader who has managed a team of 7 recruiters and delivered rapid, multi-location hiring across both direct and contract placements. Partners closely with client-side HRBPs and C-suite stakeholders, including handling contract negotiations, and has improved hiring outcomes in challenging geographies through relocation and sign-on incentive strategies.”

Pravalika Badam

Screened

Mid-level QA/SDET Automation Tester specializing in UI, API, mobile, and cloud testing

Houston, TX5y exp

“SDET focused on end-to-end quality for web applications, owning UI/API/regression automation from framework design through CI/CD integration. Notably prevented a production payment/checkout incident by adding API validations that caught incorrect tax calculations (rounding logic) during CI before release, and has a track record of stabilizing flaky Cypress tests via robust selector and wait strategies.”

srilekha pothula

Screened

Mid-level Data Engineer specializing in cloud data pipelines for healthcare and financial services

Bloomfield, CT4y exp

“Data engineer with ~4 years of experience (Cigna) building and operating Azure Data Factory pipelines for healthcare claims/member/provider data at 2–3M records/day. Emphasizes reliability and downstream safety via schema/data-quality validation, quarantine workflows, idempotent processing, and backfills; also improved runtime ~20% through SQL optimization and served curated datasets through versioned views and well-documented, analyst-friendly interfaces.”

“Data-focused engineer who has built near real-time trending news sentiment pipelines end-to-end (API/web ingestion, validation, transformations, and dashboard serving) and implemented reliability patterns like retries with exponential backoff and backfills. Also shipped Java/Spring Boot REST APIs backed by SQL with indexing/pagination, and stood up an early-stage QR-based attendance MVP using Firebase with iterative hardening via logging and validation.”

“Data Engineer (TCS) who owned an end-to-end CRM analytics pipeline for Bayer’s eSalesWeb integration, ingesting from Salesforce APIs/databases/S3 and serving analytics-ready datasets via PostgreSQL/S3 for Tableau. Drove measurable outcomes: ~60% reduction in manual data-quality effort, ~30% lower latency through SQL optimization, and ~35% improved stability via monitoring, retries, and idempotent processing.”

Teage Johnson

Screened

Junior Full-Stack Engineer specializing in FinTech and machine learning

California, USA3y exp

“Software engineer at early-stage startup Cari with hands-on experience shipping AI-enabled production workflows, including an LLM chatbot for a micro-transit platform and an automated image-processing pipeline integrated with Claude. Stands out for combining practical agent reliability patterns—schema validation, fallbacks, caching, and idempotency—with strong ML evaluation instincts and experience cleaning messy operational invoice data.”

varshini yaganti

Screened

Mid-level Data Analyst specializing in financial and customer analytics

Marietta, GA4y exp

“Analytics professional with experience at KPMG and Robosoft Technologies, working across financial and customer engagement data. They combine SQL, Python, experimentation, and BI dashboards to turn messy multi-source data into decision-ready insights, including a pricing test that improved conversion rates by 9%.”

“HRIS/payroll support professional with hands-on Workday experience focused on pre-payroll validation, compensation data accuracy, and reconciliation. They stand out for catching effective-date and compensation mismatches before payroll cutoff, building practical Excel audit tools, and supporting merit/bonus cycles through reporting, exception management, and testing.”

“Healthcare AI engineer with hands-on ownership of production ML and LLM systems at McKesson, spanning clinical risk prediction and RAG-based documentation tools. Stands out for combining deep clinical-data experience, HIPAA-aware deployment practices, and measurable impact through reduced readmissions, clinician workflow gains, and 20% to 30% faster ML delivery for engineering teams.”

Phani K

Screened

Mid-level AI/ML Engineer specializing in GenAI, NLP, and healthcare-financial ML

Terre Haute, IN4y exp

“ML/AI engineer with hands-on experience shipping healthcare AI systems, including an oncology risk prediction platform and RAG-based clinical decision support tools. Stands out for combining clinical domain context with strong production engineering across Spark, FastAPI, AWS SageMaker, monitoring, evaluation, and safety guardrails.”

“Built an AI-powered document extraction system at Copart that moved from initial POC to rollout across multiple yards, with customer-driven iteration including batch processing. Has hands-on full-stack experience spanning Java/Spring Boot backends, React frontends, and LLM-based workflows, with attention to reliability through multi-LLM fallback, retries, and post-processing.”

Pratik Patil

Screened

Senior Frontend Engineer specializing in React and FinTech SaaS

New York, NY6y exp

“Senior engineer at BDI Plus who is already using AI coding tools as a core part of daily development, with hands-on experience building enterprise LLM products such as ChatCDP AI for Morgan Stanley analysts. Particularly strong at making AI systems trustworthy and usable through auditable outputs, streaming UX, and resilient state-machine-driven error handling.”

“ML engineer at CGI who built demand forecasting models end-to-end, from feature engineering and training through AWS deployment. Stands out for a production-first mindset and strong skepticism of AI-generated code, including catching a Copilot-generated SQL query that would have caused a costly full table scan in production.”

“Cross-functional operator who regularly leads globally distributed work and acts as a bridge between product, UX, and analytics. Has driven reporting/dashboard and workflow automation initiatives with senior leadership, using data-backed communication and quick wins to improve adoption and efficiency.”

“Data scientist with hands-on experience building an XGBoost-based customer segmentation/churn risk scoring model used by sales and marketing teams. Emphasizes production-grade practices—efficient SQL for large-scale data pulls, rigorous data validation/testing, and scalable, modular Python code designed to support multiple customer types.”

“QA automation engineer who owned an end-to-end automated regression suite for a PlayStation digital store flow (login through checkout/payment), building a hybrid POM/data-driven framework from scratch with Selenium/TestNG/Cucumber and also using Playwright/TypeScript and Cypress. Integrated the suite into Jenkins CI/CD with nightly runs and reporting, improved coverage (happy + negative paths), and reduced release risk by catching critical issues like session timeout and transaction/payment defects before production.”