Vetted ETL Professionals

“Serverless-focused backend/data engineer who has delivered production Python services on AWS (FastAPI on Lambda/API Gateway) plus Glue-based ETL pipelines from S3 to relational databases. Strong in operational reliability (timeouts, retries, monitoring/alerts) and modernization work, including parallel-run parity validation for migrating legacy batch logic to Python services. Demonstrated measurable SQL tuning impact (15 min to under 3 min).”

“Built and shipped an LLM-powered data quality assistant that generates maintainable validation checks from metadata while executing validations via Great Expectations, exposed through FastAPI and integrated into Airflow-managed pipelines. Emphasizes production reliability (structured outputs, guardrails, monitoring, versioning, human review) and works closely with compliance/operations teams to deliver clear, auditable, user-friendly AI outputs.”

“Former Amazon intern who owned an end-to-end computer vision system to detect package anomalies in fulfillment centers, from data collection/labeling to production deployment on AWS (EC2/S3) with a Streamlit live-monitoring dashboard. Also has ML-in-production experience deploying and updating a recommendation model on Kubernetes (Minikube) with CI/CD via GitHub Actions, plus prior SDE experience with Jenkins-based pipelines and on-prem to AWS migration work using Glue.”

“Intel veteran who applied multiple linear regression and time-series drift analysis to semiconductor lithography overlay/metrology data, feeding model outputs into automated process control. Comfortable working across Python, VBA, and JMP/JSL, with a pragmatic approach to validation (RMSE + trend visualization) and data quality via close coordination with measurement/metrology teams.”

“Meta engineer who improved a critical revenue estimation dataset pipeline that was arriving ~6 days late—diagnosed via raw logs/lineage, redesigned legacy scans to only process the needed window, and shipped validation plus freshness/lag dashboards. Delivered ~50% latency reduction (to ~3 days) and regained adoption by running old/new pipelines in parallel with gated cutover and evidence-based customer communication. Applies incident-response rigor to real-time LLM/agentic workflow debugging and regularly runs developer demos/workshops.”

“Engineering leader who modernized and unified a fragmented product suite at Milestone via a multi-year cloud-native roadmap, delivering an MVP in three quarters and boosting team velocity by 40% through cross-functional squads. At Prometheum, led a trust-building hybrid architecture (AWS control plane + customer-hosted data plane) using Kubernetes to ensure sensitive enterprise data never left customer networks while remaining cloud-agnostic across providers.”

“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.”

“Built and shipped a real-time AI phone agent for small businesses that handles bookings/FAQs/messages using streaming ASR, an LLM with tool-calling, and TTS; deployed to production for multiple paying customers. Demonstrates strong applied LLM reliability practices (tool-first grounding, retrieval, hard-negative testing, and production monitoring) and experience orchestrating multi-step AI workflows with Airflow, Prefect, and AWS Step Functions.”

“Backend/data engineer with healthcare/telehealth domain experience, building patient appointment and data-processing systems on AWS. Has delivered production microservices and ETL pipelines (Flask/Celery, Glue/PySpark) with strong reliability/observability practices (JWT, retries/timeouts, Sentry/CloudWatch) and modernization experience migrating SAS workflows to Python services, including a documented 10min→30sec SQL performance win.”

“Technology/architecture leader with Accenture experience delivering data- and AI/ML-driven products, including a legal contract search solution and customer sales analytics for AWS. Known for scaling distributed teams (onshore/offshore), making pragmatic architecture decisions, and solving hard data problems (proprietary sources, data quality) while implementing scalable integrations like Redshift-to-Salesforce via parallelized pipelines.”

“Data engineer focused on building and operating reliable Airflow-orchestrated pipelines into BigQuery, including daily billing ingestion (~1GB/day) and ad platform (Facebook/LinkedIn) data collection. Implemented end-to-end data quality checks plus org-wide incident response automation integrating PagerDuty, Slack, and Jira, and has experience executing large backfills (4–5TB) via time-window batching.”