Vetted Data Quality Professionals

“Data engineer focused on large-scale ETL/ELT pipelines across cloud stacks (GCP and AWS), including Spark-based transformations and orchestration with Airflow. Has experience loading up to ~2TB per BigQuery target table and designing atomic loads to multiple downstream systems (Elasticsearch + Kafka), with Kubernetes deployment and Jenkins CI/CD.”

“Data engineer with experience at Bloomberg, UBS, and Bank of America building high-volume financial data platforms and services. Owned an end-to-end pipeline processing ~150–200M records/day (Kafka/Cassandra/S3 → Spark/PySpark → Snowflake) with strong data quality controls and Airflow reliability practices, reporting ~99% reliability and major performance gains. Also built large-scale external API ingestion with compliance-minded rate limiting, schema versioning, and quarantine/validation layers.”

Abhay Murjani

Screened

Director-level Data Science Manager specializing in ML forecasting, experimentation, and MLOps

New York, NY6y exp

“Data/ML engineer with experience at American Express and Amazon, owning an end-to-end rewards redemption/liability ML pipeline (~200GB) with rigorous regulatory/audit validation and quarterly executive reporting. Also built web-scraped product datasets with anti-bot protections at a startup and helped modernize an authn/authz service using AWS, plus led early-stage migration work from an internal warehouse to GCP with CI/CD and cloud observability.”

“Product leader at Nike who launched an AI-powered conversational assistant for wholesale partners on Nike.net, using LLMs, RAG, guardrails, and human escalation to balance innovation with trust. Demonstrates strong human-centered AI judgment, measurable product impact, and a practical philosophy of using AI to augment rather than replace people.”

“Data/analytics engineer who built and operated a DocuSign-based real-time analytics platform end-to-end, processing 20–50k webhook events/day with ~99.5% reliability. Strong in idempotent event processing, schema-evolution-safe ingestion (raw JSON + dynamic parsing), and serving data via versioned, low-latency REST APIs with solid CI/CD and observability.”

“Engineering leader focused on large-scale enterprise SaaS and MDM platforms, with experience modernizing monoliths into microservices, improving reliability, and scaling systems to support 15M devices across AWS and Azure. Stands out for combining deep platform architecture work with strong org-building: managed teams up to 45 globally and built a 0-to-1 platform services team to 22 people in under a year.”

“BlackRock application engineer/product owner focused on enterprise AI platforms, building internal GenAI and ML workflow products for operations and business teams. Stands out for combining consultative solution design with hands-on implementation, including a contract review platform that cut first-draft review time by 60%+ and an AI mailbox tool that drew interest from 17 additional teams during the POC stage.”

Harsh Sanas

Screened

Intern Full-Stack Engineer specializing in AI and distributed systems

Los Angeles, CA2y exp

“Full-stack product engineer who has designed and shipped production web experiences in EV charging, trading, automotive companion apps, and AI systems. Stands out for owning user-facing React experiences through backend integration and production monitoring, with a strong bias toward reliability in real-time and high-stakes workflows. Also has early-stage Scale AI experience building a Text-to-SQL agent stack with Python, PostgreSQL, Redis, Kafka, and AWS.”

“Backend/data engineer with hands-on production experience across Python/FastAPI services and AWS (Lambda, API Gateway, SQS, ECS) delivered via Terraform and GitHub Actions. Built Glue-to-Redshift ETL pipelines with Step Functions retry/catch patterns, schema evolution safeguards, and data quality checks; also modernized a legacy SAS monthly reporting system into Python microservices with rigorous side-by-side parity validation. Demonstrated strong SQL tuning skills with a reported improvement from 5 minutes to 15 seconds.”

“Analytics professional with experience at McKinsey & Company and Dell Technologies, focused on turning messy operational and business data into trusted dashboards and decision tools. They combine SQL, Power BI, and Python to solve data quality issues, define metrics like retention, and deliver measurable impact such as a roughly 30% reduction in manual reporting time.”

“ML/AI engineer with hands-on ownership of production systems spanning classical ML fraud detection and GenAI agent workflows. At Fidelity, they built an end-to-end fraud platform that improved review queue Precision@K by 15-20% while reducing false positives 10-15%, and they also shipped RAG-based agent systems that cut manual workflow effort by 30-40%.”

“Data engineer at Plaid who built greenfield, end-to-end real-time transaction pipelines and FastAPI data services for fraud detection and analytics, handling millions of events per day. Strong focus on reliability and data integrity via Great Expectations validation, Airflow-based monitoring/SLAs, quarantine/staging patterns, and robust external data ingestion with schema versioning and backfills (reported 50% fewer anomalies and ~40% fewer failures).”

Jyothi Kumar

Screened

Senior Software Engineer specializing in full-stack enterprise SaaS and digital marketing

Mountain View, CA10y exp

“Atlassian engineer who grew from frontend into full-stack ownership, spanning React/TypeScript UI architecture, backend data pipelines, observability, and Kotlin/Spring Boot integrations. They describe owning Confluence profile-service integration tied to signup growth, plus building a marketing analytics platform with caching, reusable components, and data-quality tooling that increased stakeholder adoption by 30%.”