Vetted Looker Professionals

Phanideep P

Screened

Senior Data Engineer specializing in cloud lakehouse and streaming data platforms

5y exp

“Data platform/data engineer with cross-industry experience in banking and healthcare, building cloud-native lakehouse architectures across AWS/Azure/GCP. Has owned high-volume (millions of records; TB/day) pipelines with strong data quality automation (dbt/Great Expectations), observability (Grafana/Prometheus), and real-time streaming (Kafka/Spark) for fraud monitoring; also delivered an early-stage migration from SQL Server to BigQuery with 40% batch latency reduction.”

Rajeshwar Peri

Screened

Mid-level Data Analyst specializing in healthcare and financial analytics

Chicago, IL5y exp

“Healthcare analytics candidate with hands-on experience turning messy claims and CRM data into validated reporting tables, automating monthly reporting in Python/Airflow, and operationalizing churn metrics in SQL and Tableau. They appear especially strong in stakeholder-aligned metric design and delivered a reported ~10% churn reduction through cohort analysis, segmentation, and at-risk member targeting.”

“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.”

Monisha Nettem

Screened

Mid-level AI/ML Engineer specializing in LLMs, RAG, and MLOps

USA5y exp

“AI/ML engineer with banking domain experience (M&T Bank) who built a production credit-risk prediction and reporting platform combining ML models (XGBoost/TensorFlow) with a RAG pipeline (LangChain + GPT-4) over compliance documents. Delivered measurable impact (≈20% better risk detection/precision, 50% less manual reporting) and productionized workflows on Vertex AI/Kubeflow with CI/CD and monitoring; also implemented embedding-based semantic search using FAISS/Pinecone.”

“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%.”

“Enterprise healthcare SaaS implementation and customer success leader focused on OR/clinic scheduling optimization, owning end-to-end deployments from discovery through training, adoption, and renewal across multi-hospital systems. Strong analytics/ROI storytelling (Tableau/Looker, QBRs) linking AI-driven outreach and block release workflows to increased case volume and improved block utilization, and experienced partnering with Product/Sales to resolve integrations and drive roadmap/upsell outcomes.”

Santhoshi Priya Sunchu

Screened

Mid-level Data Scientist specializing in NLP and predictive modeling

Massachusetts, USA5y exp

“AI/ML practitioner in healthcare/insurance (Blue Cross Blue Shield) who built and deployed a production NLP system to classify patient risk from unstructured clinical notes. Experienced in end-to-end pipeline orchestration (Airflow, AWS Step Functions/Lambda/SageMaker) and real-time optimization (BERT to DistilBERT on AWS GPUs), with strong clinician collaboration to drive adoption.”

PAVEL A ASPIAZU

Screened

Senior Paid Media (SEM/PPC) Manager specializing in Google Ads and Meta performance marketing

McLean, VA26y exp

“Paid media performance marketer with agency experience owning a high-spend account for ArcPoint Labs, running multi-channel Google Ads (search/display) and Meta campaigns to drive medical testing appointments and sales. Reported lifting ROAS from ~2x to ~6–7x within ~18 months while supporting expansion from 30 to 60 U.S. locations, using disciplined attribution, constant A/B testing, and structured campaign health audits.”

Shabari Vignesh

Screened

Mid-level Data Engineer specializing in cloud data platforms and AI agents

Santa Clara, CA6y exp

“Data/Backend engineer who has owned end-to-end merchant analytics systems on AWS: orchestrated multi-source ingestion (FISERV/Shopify/Clover) with Step Functions/Lambda, enforced strong data quality gates, and served curated datasets via Redshift and a FastAPI layer. Also built an early-stage Merchant Insights AI agent that converts natural language questions into SQL using OpenAI models, with full CI/CD and observability.”

“Merchandising/brand partnerships professional with Hot Topic experience sourcing and scaling licensed collaborations (e.g., Monster High with Mattel) by using social/customer demand signals and post-launch sell-through/ratings analysis. Strong in vendor/licensing negotiations to improve wholesale costs and margins, and in data-driven assortment planning with weekly performance reviews and seasonality-based inventory strategies.”

Vanessa Avilez

Screened

Senior Lifecycle & Retention Marketing Manager specializing in CRM and subscriber growth

Los Angeles, CA13y exp

“Lifecycle/CRM marketer with hands-on Braze automation experience, running cross-channel (email + push) onboarding and milestone campaigns for an app-based content product. Demonstrated strong activation metrics on Day 0 and drove a 10% week-over-week churn reduction by partnering with Customer Service on winback save-rate and scripting tests.”

Lakshmi Priya Ramisetty

Screened

Mid-level ML & Data Engineer specializing in GenAI, graph modeling, and fraud/risk analytics

Redwood City, CA5y exp

“Built a production AI fraud/risk scoring platform at BlueArc that ingests web business/product/site data, generates text+image embeddings, and connects entities in a graph to detect reuse patterns and links to known bad actors. Optimized for scale with incremental graph re-scoring and delivered investigator-friendly explainability by surfacing the exact signals/relationships behind each score; orchestrated workflows with Airflow and GCP event-driven components (Pub/Sub, Dataflow, Cloud Run) and has recent LLM workflow orchestration experience (retrieval, prompting, scoring).”

“Built and deployed a production LLM/RAG clinical document understanding and summarization system for healthcare, focused on reducing manual review time while meeting strict accuracy, latency, and compliance needs. Demonstrates strong MLOps/orchestration depth (Airflow, Kubernetes, Azure ML Pipelines) and a rigorous approach to hallucination mitigation through layered, source-grounded safeguards and stakeholder-driven requirements with physicians/compliance teams.”

“Data engineer with healthcare/clinical trial domain experience who owned a 100TB+/month AWS pipeline end-to-end (Glue/S3/Redshift/Airflow) and drove measurable outcomes (20% lower latency, 99.9% reliability, 40% less manual reporting). Also built production data services and API-based ingestion on GCP (Cloud Run/Functions/BigQuery) with strong validation, versioning, and safe migration practices, and launched an early-stage RAG solution (LangChain + GPT-4) for researchers.”

Brandon Nichols

Screened

Senior Customer Success Manager specializing in enterprise AI-powered SaaS

San Francisco, CA5y exp

“Enterprise B2B Customer Success professional who owns complex SaaS accounts end-to-end (onboarding, adoption, renewal, and expansion) across hundreds of users and multiple departments. Demonstrated measurable adoption gains (40–60% advanced feature engagement), early renewal/expansion (120+ days ahead), and strong cross-functional execution to unblock SSO/integration issues while consolidating 20+ subscriptions into a single enterprise contract.”

Tyler Brown

Screened

Mid-level Forward-Deployed AI Engineer specializing in LLM automation for financial services

Atlanta, GA5y exp

“Solo builder who created a real-time AI call coaching product for sales reps in just 18 days, spanning 29 stateless edge functions, 157 SQL migrations, and ~72k lines of TypeScript. They combine strong full-stack engineering with sharp product instincts, using direct user feedback and SQL-driven observability to iterate quickly and win early paying customers without marketing.”