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Data Engineer with less than a year in Data Pipelines & Real-Time Processing
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Data Engineer with hands on experience of designing and deploying scalable data pipelines, ETL workflows, and real-time data processing systems. Hands-on expertise in Python, PySpark, SQL, PostgreSQL, and Snowflake with proven ability to build edge-deployed, multiprocessing data ingestion architectures. Experienced with cloud-native data warehousing, containerized deployments using Docker, and Generative AI integration into data workflows. Adept at optimizing pipelines for high-throughput, low-latency performance on resource-constrained and distributed environments. Stanford-certified in Machine Learning.
Indian Institute of Information Technology (IIIT), Surat
B.Tech · Computer Science and Engineering
November 1, 2022 – July 1, 2026
Centre for Visual Information Technology
Data Engineer – Pipelines & Real-Time Processing
February 1, 2026 – Present
Hyderābād, Telangana, India
Real-Time Telemetry Data Pipeline – DriveGuard AI
February 1, 2025 – Present
Architected an end-to-end real-time data pipeline fusing GPS coordinates, IMU sensor streams, and computer vision detection outputs into a unified analytical data store — processing 10K+ events/day with 99%+ uptime. Executed IoU-based event deduplication and multi-frame aggregation logic to clean and normalize raw sensor outputs before loading into the PostgreSQL data warehouse. Architected the Snowflake schema for long-term historical data retention and enabled Power BI-compatible data exports for cross-team analytics consumption.
Edge AI Data Ingestion System – Raspberry Pi Deployment
December 1, 2024 – January 1, 2025
Built a distributed edge data ingestion architecture on Raspberry Pi with 5 parallel processing workers, each handling a dedicated data stream (helmet detection, lane analysis, ANPR, vehicle classification, counting). Engineered Kafka-compatible event queuing logic to buffer high-frequency data bursts and ensure zero data loss during peak traffic capture windows; containerized all modules with Docker for portable deployment.
Machine Learning Specialization: Supervised learning, unsupervised learning, and advanced ML techniques.
Stanford University / DeepLearning.AI
June 1, 2026 – Present
Power BI Certification
TNX
January 1, 2024 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a strong alignment with the target role of Data Engineer, focusing on real-time processing, ETL, and data warehousing. The diversity of projects, from real-time telemetry to edge AI data ingestion, showcases a broad skill set and adaptability. The use of various modern technologies and integration of AI components indicates a forward-thinking and innovative mindset, which aligns well with dynamic technical environments. The Stanford certification in Machine Learning further emphasizes a commitment to continuous learning and advanced technical capabilities.
Soft Skills & Operational Fit
The candidate's resume highlights strong problem-solving skills through optimizing query performance and reducing data errors. Their experience with real-time monitoring dashboards and automated alerts suggests a proactive approach to operational health. The ability to work with resource-constrained environments (Raspberry Pi) indicates adaptability and resourcefulness. The project descriptions imply a collaborative approach by enabling cross-team analytics consumption.