Vetted Data Transformation Professionals

“Analytics candidate with hands-on experience in both fraud and churn use cases, including SQL-based preparation of 6.5M transaction records and reproducible Python modeling workflows. Stands out for combining technical rigor in data quality, feature engineering, and imbalance handling with strong stakeholder alignment, metric definition, and dashboard adoption.”

“Candidate has a business plan and has been actively fundraising for about one month by reaching out to targeted VCs. They are early in learning the VC/accelerator landscape, pragmatic about entrepreneurship, and open to either founding their company if funded or joining an existing company if a strong opportunity arises.”

“Backend/data engineer focused on AWS-based Python services and data pipelines: built a Django/DRF user management/auth platform deployed with serverless AWS (Lambda/API Gateway) and event-driven workflows (Step Functions/EventBridge), with CloudFormation + Jenkins for automated delivery and Secrets Manager/Parameter Store for secure config. Also delivered AWS Glue ETL from S3 to RDS with schema evolution controls and incident-driven improvements, and has demonstrated measurable SQL tuning impact (minutes-to-seconds).”

Khoa Nguyen

Screened

Junior Software Engineer specializing in Python, AWS, and data/ETL systems

Washington, USA1y exp

“Data/ETL-focused engineer with Amazon experience building and deploying AWS-based pipelines that became the primary source of automated customer feedback insights (processing millions of records daily). Demonstrated strong incident troubleshooting across software/host/network layers using CloudWatch, traces, and metrics, plus hands-on stakeholder and on-site operator collaboration to translate reporting needs into star-schema data models and tailored Python ETL logic.”

Sushma Mangalampati

Screened

Mid-level Data Engineer specializing in lakehouse ETL and analytics engineering

Boston, MA6y exp

“Data engineer with strong end-to-end ownership of production lakehouse pipelines (Snowflake + Databricks + Airflow + dbt + Great Expectations), handling 8M+ records/month and 500K+ daily CDC updates. Delivered measurable reliability and efficiency gains (41% cost reduction, freshness improved from 4h to 30m, 35% fewer downstream incidents) and has experience building a lakehouse platform from scratch across 12 source systems.”

Yash Rangucha

Screened

Mid-level Software Engineer specializing in backend microservices and real-time streaming

Illinois, USA4y exp

“Built and owned an end-to-end LLM-powered enterprise retrieval pipeline at ServiceNow, spanning ingestion of structured/semi-structured sources through vector retrieval and real-time API serving. Focused heavily on reliability and quality (multi-stage validation, monitoring, evaluation pipelines) while also driving performance improvements (~35% faster responses) via caching, async processing, and SQL/query optimization.”

Barbara Christina Cruze

Screened

Senior Business Analytics Consultant specializing in BI, data engineering, and predictive analytics

Dallas, TX8y exp

“Healthcare analytics candidate with hands-on experience turning messy claims, enrollment, and reference data into trusted SQL reporting layers and reproducible Python workflows. They emphasize metric standardization, stakeholder alignment, and operational impact, including ~40% reduction in manual reporting effort and improved forecasting/resource prioritization through high-risk patient segmentation.”

“Analytics professional with experience across retail and healthcare, including Kroger and CVS Health. They have built SQL and Python workflows to clean and operationalize messy data, and have owned patient adherence/retention analytics projects that informed targeted interventions and improved refill and retention reporting.”

“ML/AI engineer with hands-on experience building production healthcare AI systems across predictive modeling and GenAI. They built an end-to-end patient risk prediction platform and a RAG-based clinical summarization feature, combining strong NLP/LLM skills with AWS deployment, monitoring, drift detection, and reusable Python service design to deliver measurable clinical and operational impact.”