Vetted Data Quality Professionals

“Backend/data engineer with experience on large-scale consumer platforms (Google and Meta), building high-traffic Python microservices (REST/gRPC) on Kubernetes with strong reliability/observability practices. Delivered AWS container-based deployments with CI/CD and IaC, and built AWS Glue ETL pipelines on S3 with schema evolution and data quality controls; also has demonstrated SQL tuning impact (15% latency reduction) and incident ownership for batch pipelines.”

“Early-stage founder building a service business targeting small clinics, already with one client. Identified the opportunity by helping a family member and then validating needs through direct client conversations; uses AI (including AI agents) for content generation and plans deeper workflow automation to scale cost-effectively.”

“Backend/data engineer with production experience at Uber building a near real-time driver rewards service on AWS (FastAPI, PostgreSQL, Redis) with strong reliability and concurrency controls. Also delivered AWS Lambda/ECS containerized deployments with GitHub Actions CI/CD and cost governance, built AWS Glue ETL with schema-evolution handling, and drove a ~10x SQL performance improvement while owning incident response via CloudWatch.”

“Engineering/tech leader with experience spanning major platforms (mentions Microsoft and AWS) and leading ML-driven product architecture (ranking systems). Describes driving a shift to a layered, API-standardized service architecture (including vector database components) that materially increased delivery speed (weeks to hours) while improving scalability and security, and emphasizes rigorous execution reviews (QDR/OKRs) plus transparent remote-team operating rhythms.”

“NVIDIA engineer who built and shipped a production LLM-powered enterprise knowledge system (summarization, transcription, and Q&A) that cut document retrieval time ~30%. Deep hands-on experience with RAG (FAISS/Pinecone), GPU-accelerated microservices on AWS, and reliability/safety practices (Guardrails AI, prompt A/B testing, canary releases) plus strong MLOps orchestration across Airflow, Step Functions, and Kubernetes GitOps.”

“Entrepreneurially minded builder focused on proving architecture concepts via minimal demo prototypes for marketing. Has hands-on experience improving an A/B experimentation framework by interviewing stakeholders, identifying system limits and bottlenecks, and defining success criteria to scale experimentation and speed up analysis.”

“Backend/data engineer with hands-on production experience across GCP (FastAPI microservices on Kubernetes) and AWS (Lambda, ECS Fargate, Glue). Has modernized legacy SAS batch systems into Python services with parallel-run parity validation, and has strong operational rigor in ETL reliability/monitoring plus proven SQL tuning impact (25s to <300ms, ~60% CPU reduction).”

“BCG consultant with repeated "founder-level" ownership of large, cross-functional initiatives, including a financial services data management transformation delivering +30% client satisfaction and -60% issue resolution time. Also led portfolio strategy for a $160B biopharma client, building board-level models and aligning stakeholders across five segments to drive a 23% price increase and improved post-merger cost structure.”

“Backend/ML platform engineer from Snapsheet who owned production Python services and data pipelines for insurance claims, including an AI document classification/summarization FastAPI service on ECS/Fargate processing 1M+ documents/year. Strong in AWS infrastructure (Terraform, CI/CD, secrets/IAM, autoscaling), Glue/PySpark ETL with schema evolution controls, and legacy SAS-to-microservices modernization with safe, feature-flagged rollouts and measurable performance wins.”

“PMO consultant with experience delivering regulatory and compliance-driven programs across financial services and government healthcare, including Citibank MRA remediation (Helios) and Ohio Department of Medicaid enterprise release management. Known for proactively consolidating fragmented project data into executive-ready reporting, driving cross-functional alignment, and operating with strong discretion in regulated environments.”

“Engineering/data platform leader from FLYR (airline ML forecasting and automated pricing) who built scalable ingestion/ETL and a canonical data model to onboard airlines with highly heterogeneous source systems. Created a golden-metrics layer for airline KPIs and implemented monitoring/backfill capabilities, cutting onboarding time by 50%+ while improving SLA performance and controlling cloud/ML training costs through stronger data quality gates.”