Pre-screened and vetted in the DFW Metroplex.
Junior Data Analyst specializing in SQL, Python, and BI analytics
Mid-level Data Analyst / Business Analyst specializing in healthcare and operations analytics
Mid-level Data Engineer and Analytics Analyst specializing in business growth and marketing insights
“Analytics professional with operations-grounded experience at WWEX Group who built a Snowflake/dbt fleet-efficiency data model combining telematics, ERP, and driver logs into near real-time executive reporting. They pair strong SQL/Python workflow automation with practical stakeholder enablement, and cite measurable impact including cutting reporting time from 72 hours to 15 minutes and helping drive $450K in quarterly fuel savings.”
Mid-level AI Data Engineer specializing in GenAI, RAG, and cloud data pipelines
“LLM/agentic AI builder who deployed a production ITSM automation agent on Google ADK integrating ServiceNow and FreshService, with strong safety guardrails (human-approval gating and runbook-only command execution) and rigorous evaluation (500 synthetic tickets; 80%+ false-positive reduction). Also partnered with finance to deliver an AI agent that automated invoice/SOW retrieval and monthly reporting to account managers, reducing manual back-and-forth.”
Mid-level Data Engineer specializing in cloud-native batch and streaming pipelines
“Data/ML platform engineer with ~6 years in financial services and enterprise data platforms, building regulated fraud/credit-risk pipelines on AWS (Airflow, EMR/Spark, MLflow) and an Azure lakehouse ingesting 50+ sources and serving ~100M records/day. Also led an early-stage deployment of a RAG-based internal AI search tool using AWS Bedrock and LangChain with automated evaluation to validate LLM accuracy.”
Junior AI Data Engineer specializing in Azure Databricks lakehouse and GenAI RAG systems
“Backend/applied AI engineer from Cloud Rack Systems who built production GenAI/RAG and data platforms on Azure/Databricks at enterprise scale (2.5M records/day). Known for making LLM systems behave like deterministic services via strict retrieval contracts, citation-based validation, and strong observability—shipping a knowledge assistant used daily by 50+ users while driving hallucinations near zero and materially improving latency and cost.”
Mid-level Data Scientist specializing in ML, MLOps, and Generative AI
Mid-level Business Analyst specializing in recruitment analytics and BI automation
Mid-level Data Engineer specializing in cloud-native batch and streaming pipelines
Mid-level Data Analyst specializing in SQL/Python analytics, ETL pipelines, and BI dashboards
“Data/AI practitioner who built a production LLM-driven healthcare claims analytics and dashboarding system to reduce avoidable ER visits—processing 1.4M+ claims, flagging 19% as non-emergent, and projecting ~$2.8M in annual savings. Demonstrates strong real-world LLM reliability and performance engineering (grounding, numeric validation, caching, materialized views, quantization) plus orchestration experience with Airflow and Azure Data Factory.”