Raghu Para is a seasoned technology leader with over 15 years of experience at the intersection of data engineering, AI/ML, and cloud platform architecture. Currently serving as a Senior Cross/Multi-Platform Engineer at Ford Motor Company, he brings a unique blend of technical depth and business acumen to drive impactful, scalable solutions across enterprise environments.
Raghu specializes in data analytics, machine learning, and cloud-native development, with hands-on expertise in Google Cloud Platform (GCP), Python, Node.js, Kubernetes, and modern data stack tools such as Airflow, Kafka, and Spark. His core focus lies in AI-powered data quality management, anomaly detection, and automation of data governance processes, which has led to multimillion-dollar cost savings and enhanced decision-making capabilities for enterprise stakeholders.
Career Highlights:
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AI-Driven Data Quality Automation: Led the creation of an intelligent metadata profiling framework using GPT Large Language Models, Autoencoders, and Random Forests to automate rule recommendations. This reduced manual workload by 40% and improved governance by 20%.
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Cost-Efficient AI Integration: Integrated OpenAI’s Batch API to optimize token usage for large-scale metadata processing, achieving $5.4 million in annual savings.
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Cloud-Native Data Pipeline Engineering: Architected robust pipelines in GCP using Cloud Run, BigQuery, DataProc, and Airflow Astronomer, enabling 10TB+ of daily data profiling and accelerating time-to-insight by 40%.
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Operational Intelligence in Automotive Analytics: Created Alteryx workflows and star schema models for modem activation tracking and vehicle performance analytics, contributing to $150M+ in dealer sales reconciliation.
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Cross-Platform Collaboration: Designed reusable cloud functions and message-driven integrations using Pub/Sub, enabling seamless collaboration across 100+ business teams and reducing deployment times by 60%.
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Advanced Statistical Analysis: Conducted in-depth vehicle energy consumption analysis and discrepancy reporting between global data sources, improving accuracy for reporting and billing.
Technical Expertise:
Languages & Frameworks: Python, Go, JavaScript, Node.js
Cloud Platforms: GCP (BigQuery, Cloud Functions, Dataflow, Cloud Run), Kubernetes
Data & ML Tools: Airflow, Spark, HBase, Hadoop, Kafka, Alteryx, QlikView, SAS, IBM SPSS
DevOps & CI/CD: Docker, Jenkins, Git, Apigee, Microservices Architecture
Statistical Modeling & AI: Natural Language Processing, LLMs, Autoencoders, Random Forests
Thought Leadership & Media:
Raghu’s insights and expertise have also been featured on platforms like Muck Rack, where he contributes thought leadership articles on data science, artificial intelligence, and engineering best practices.