I am an innovative and results‑driven data engineering and cloud solutions specialist with proven expertise in designing, implementing, and automating large‑scale data pipelines across hybrid cloud environments (AWS & Google Cloud). My work spans orchestrating secure, high‑volume data transfers, real‑time analytics, and predictive modeling for IoT and supply‑chain use cases. I thrive on deriving actionable insights from time‑series sensor data for risk assessment, anomaly detection, and forecasting.
Build end‑to‑end ETL/ELT pipelines from TimescaleDB & PostgreSQL to AWS S3, Google Cloud Storage, and BigQuery using Lambda, Dataflow, Pub/Sub, and Cloud Functions.
Deep experience with AWS (Lambda, S3, EventBridge, IAM) and Google Cloud Platform (BigQuery, Dataflow, Pub/Sub, Cloud Functions, Vertex AI) to deliver scalable solutions.
Collect, transform, and stream high‑frequency sensor data at scale (~1.5M rows every 15 minutes) for real‑time analytics and monitoring.
Design robust schemas, perform SQL‑based transformations, combine multiple BigQuery tables, and apply business logic (e.g., temperature excursion risk scoring).
Develop LSTM, ARIMA, and CNN‑LSTM models for time‑series forecasting and anomaly detection to improve supply‑chain resilience.
Design frameworks to score and monitor risks like temperature excursions, transportation delays, and security risks for pharmaceutical shipments with configurable thresholds.
Automate Lambda deployments with zipped dependencies, environment‑specific configurations, and version control to ensure reliable releases.
AWS (Lambda, S3, EventBridge), GCP (BigQuery, Dataflow, Cloud Functions, Pub/Sub, Vertex AI)
TimescaleDB, PostgreSQL, BigQuery
AWS EventBridge, Pub/Sub, Dataflow pipelines, Cloud Functions
Python (pandas, NumPy, TensorFlow, joblib), SQL (BigQuery SQL, PostgreSQL)
TensorFlow/Keras, ARIMA, LSTM, CNN‑LSTM models
Automated Lambda deployments, environment management, version control
FastAPI, Flask for serving ML models and BigQuery data APIs
BigQuery Console, GCP Console dashboards
Designed and implemented an end‑to‑end pipeline to extract sensor data from TimescaleDB and transfer it to AWS S3 via Lambda, then to Google Cloud Storage, and finally ingest into BigQuery using Dataflow. Automated deployments using CI/CD ensured seamless updates while incremental loads were achieved by storing last‑timestamp checkpoints in S3.
Developed a BigQuery and Cloud Function system to combine multiple tables and calculate risk scores for temperature excursions in pharmaceutical shipments. Implemented a real‑time risk scoring algorithm with configurable thresholds (15, 30, 60 minutes) to classify shipments into red, yellow, and green zones.
Built LSTM‑based models to forecast temperature excursions using 15‑minute interval sensor data. Experimented with ARIMA and CNN‑LSTM hybrid models to improve anomaly detection and reduce false negatives in sudden excursions.
Deployed trained Vertex AI models via FastAPI endpoints to receive live input temperature values and return predictive outputs for proactive shipment monitoring. This enabled real‑time decision‑making for supply chain stakeholders.
Created serverless APIs with API‑key authentication to query BigQuery for shipment and sensor data by time windows and organization IDs. Enabled flexible querying (last 24 hours, last X hours) and automatic UTC timestamp handling for consistency across systems.
Location: Jammu, Jammu and Kashmir, India
Email: alkagupta48@gmail.com
LinkedIn: Connect on LinkedIn
Feel free to reach out to discuss how I can help your organization leverage data engineering and analytics for smarter decision‑making and operational excellence.