Vetted Data Warehousing Professionals

“QA engineer intern experience at Amazon (Alexa Daily Essentials) owning end-to-end quality for AI-powered timer/stopwatch features at massive scale. Demonstrates disciplined Jira-based workflow, automation-driven regression coverage, and strong device-matrix verification (Echo Show generations), with concrete examples of finding and driving resolution of complex UI/backend synchronization bugs.”

“Full-stack engineer who has shipped and owned production analytics dashboards using Next.js App Router + TypeScript, combining server components for data-heavy pages with client components for interactive charts/filters. Also built a Temporal-orchestrated payment reconciliation workflow with versioning, idempotency, and exponential-backoff retries, and has hands-on Postgres query/index optimization using EXPLAIN ANALYZE.”

“Data engineer/backend data services practitioner with Bank of America experience building real-time and batch transaction-monitoring pipelines and APIs (Kafka + databases, REST/GraphQL). Highlights include a reported 45% response-time improvement through performance optimizations and use of Delta Lake schema evolution plus CI/CD (GitHub Actions/Jenkins) and operational reliability patterns like CloudWatch monitoring and dead-letter queues.”

“Data engineer focused on building reliable, production-grade pipelines and external data collection systems on AWS (S3/Lambda/SQS/Glue/EMR) using PySpark/SQL, serving curated datasets to Snowflake/Redshift for finance and fraud teams. Has operated a large-scale crawler ingesting millions of records/day with anti-bot tactics, schema versioning/quarantine, and CloudWatch/Datadog monitoring, and also shipped a versioned REST API with caching and query optimization.”

“Data/ML platform engineer with healthcare (Cigna) experience owning an end-to-end pipeline spanning Airflow + Debezium CDC ingestion, PySpark/SQL transformations, rigorous data quality gates, and feature-store/API serving for ML training and inference. Worked at 10+ TB scale and cites a ~30% latency reduction plus stronger reliability via idempotent design, monitoring, and backfill-safe reprocessing; also built pragmatic early-stage data pipelines at Frankenbuild Ventures.”

“Data engineer/platform builder who has owned production pipelines end-to-end processing millions of records/day, with strong emphasis on data quality (quarantine workflows) and reliability (monitoring, retries, incremental loads). Also designed large-scale external data collection/crawling with anti-bot handling and backfills, and shipped versioned REST data services optimized for performance and developer usability in an early-stage environment.”

Saicharitha Yanamandala

Screened

Mid-Level Software Developer specializing in Java, Cloud, and Microservices

Chicago, IL6y exp

“Backend/Python engineer who owned an end-to-end FastAPI + AWS internal natural-language document Q&A system (Textract extraction, embeddings/vector DB, LLM integration) with strong focus on reliability and latency. Hands-on with Kubernetes + GitOps (Argo CD, Helm, rolling updates/auto-rollback) and built/optimized Kafka streaming pipelines using Prometheus/Grafana. Also supported a zero-downtime on-prem to cloud migration with parallel run and gradual traffic cutover.”

Shweta Gupta

Screened

Senior Backend Software Engineer specializing in Java microservices, Kafka, and AWS

Seattle, WA6y exp

“AI engineer who shipped a production chat assistant for a storage company by building the underlying RAG-style knowledge base (document ingestion, chunking/embeddings, FAISS vector store) and an admin update interface to keep content current. Also has full-stack delivery experience (Python REST APIs + React/TypeScript UI) and AWS operations using Terraform/Jenkins, including handling a real production performance incident by optimizing DB queries and adding auto-scaling.”

Nikitha Margadi

Screened

Mid-level Data Engineer specializing in cloud lakehouse, streaming, and MLOps

Texas, USA5y exp

“Data engineer at AT&T focused on large-scale telecom (5G/IoT) data platforms, owning end-to-end pipelines from Kafka/Azure ingestion through Databricks/Delta Lake transformations to serving analytics and ML. Has operated at very high volumes (~50+ TB/day) and delivered measurable performance gains (25–30% faster processing) plus improved reliability via Airflow monitoring, robust data quality checks, and resilient external data collection patterns (rate limiting, retries, dynamic schemas).”

Krishnamraju Penumatsa

Screened

Mid-level Data Engineer specializing in cloud data platforms and streaming pipelines

Fort Worth, TX6y exp

“Data engineer currently at American Airlines who built and owned end-to-end flight operations and booking data pipelines (batch + real-time) using Azure Data Factory, Kafka, Spark/Databricks, Synapse, and Snowflake—processing hundreds of GBs/day. Strong focus on reliability and data quality (idempotency, checkpointing, retries, validation/alerts) and delivered near-real-time analytics powering Power BI dashboards; previously helped stand up an early-stage data platform at Sysco on AWS (Glue/S3/Redshift) with Airflow and Jenkins CI/CD.”

“Built and deployed an agentic RAG assistant in production to automate enterprise knowledge search and multi-step workflows with tool calling, tackling real-world issues like hallucinations, retrieval accuracy, and latency. Demonstrates strong LLMOps and orchestration depth (MLflow, Airflow, LangGraph/LangChain/LlamaIndex) plus a metrics-driven approach to agent testing/evaluation and cross-functional delivery with business stakeholders.”

“Healthcare analytics candidate with hands-on experience turning messy claims and clinical data into validated SQL/Python pipelines and Power BI dashboards. They have delivered measurable impact in revenue cycle operations, including 15-18% improvement in reimbursement accuracy and 40-45% reduction in manual reporting effort.”

“Backend engineer with experience at HSBC and Machinations who has delivered major production performance wins (cutting large trade-file upload times from ~13–15s to ~2s) using chunked parallel processing with strong reliability controls. Also built and shipped an applied AI RAG workflow using Langflow + Cohere embeddings + FAISS with hosted/local LLM fallbacks (Hugging Face, Ollama) and production-grade guardrails, observability, and evaluation.”

Sowmya Medabalimi

Screened

Junior AI/ML Engineer specializing in generative AI and FinTech

Denton, TX3y exp

“Product-minded engineer/designer with hands-on experience shipping AI-powered web experiences, including a financial assistant at Intuit, a document verification platform at Mphasis, and a healthcare companion chatbot. Stands out for combining UX design, frontend implementation, and retrieval/LLM backend work to turn ambiguous AI concepts into simple, production-oriented user experiences.”

“Applied NLP/ML engineer with experience at KeyBank and Novartis building production document intelligence and entity-resolution systems in finance and healthcare. Has delivered end-to-end pipelines (Airflow + AWS) using transformers (DistilBERT/Sentence-BERT), vector search (FAISS/Milvus/Pinecone), and human-in-the-loop labeling to achieve measurable gains (40%+ faster queries; up to 88% F1 and 93% precision/90% recall in entity linking).”

“Engineering leader focused on developer platforms and open-source frameworks/SDKs, with strong community and release-engineering chops. Drove major reliability and DX improvements (30–50% faster release cycles; 2–3x repeat contributors; ~50% faster onboarding) and led an incremental Python monolith to TypeScript event-driven migration using Protobuf contracts, feature flags, and a plugin architecture to preserve backward compatibility.”

“Built an end-to-end GenAI underwriting copilot at TD Bank for complex financial documents, combining RoBERTa-based risk classification with Azure OpenAI RAG to deliver grounded, citation-based insights. Drove a 40-50% reduction in manual underwriting review time and created reusable FastAPI ML services that cut integration effort for other teams by 30-40%.”