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Vetted Data & Analytics Professionals in the DFW Metroplex
Pre-screened and vetted in the DFW Metroplex.
Popular Titles
Senior Data Engineer specializing in Azure lakehouse and healthcare interoperability
Mid-level AI/ML Engineer specializing in MLOps and LLM applications
Mid-level Data Scientist specializing in financial risk modeling and healthcare analytics
Mid-level Business Systems Analyst specializing in payroll and workforce reporting
Mid-level Data Engineer specializing in cloud lakehouse, ETL automation, and healthcare analytics
Senior Product Business Analyst specializing in FinTech and SaaS implementations
Mid-level Data Engineer specializing in cloud ETL/ELT and data warehousing
Mid-level Business Analyst specializing in data-driven enterprise systems
Mid-level Data Analyst specializing in business analytics across regulated industries
Mid-level Data Engineer specializing in cloud data platforms and streaming pipelines
Senior Business Analyst / QA Lead specializing in cloud, security, and enterprise testing
Mid-level Data Scientist specializing in ML, data engineering, and real-time analytics
Jaideep Janapati
Mid-level Data Engineer specializing in cloud data platforms and real-time pipelines
“Data engineer who has owned production pipelines end-to-end—from Kafka/Airflow ingestion through SQL/Python validation and dbt transformations into Redshift/BI. Also built and operated a large-scale distributed web scraping platform (50–100 sites daily, ~5–10M records/day) with Kubernetes, Kafka queues, robust retries/DLQ, anti-bot measures, and backfill-safe raw HTML storage.”
Mid-level Systems Business Analyst specializing in AI automation and financial data pipelines
Senior Data Engineer specializing in Azure/AWS lakehouse and real-time analytics
Mid-level Supply Chain Analyst specializing in procurement and supplier performance
Junior Data Analyst specializing in SQL, Python, and BI analytics
Mid-level Data Analyst / Business Analyst specializing in healthcare and operations analytics
Hetal Pambhar
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.”
Ajith Kumar
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.”
VIJAY SAGI
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.”
Vengalarao Pachava
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.”