---
title: "Kartik B Bhargav - Senior Analytics Consultant in India | Tal"
description: "Meet Kartik B Bhargav, Senior Analytics Consultant @ Quantzig | Azure Certified | MLOps, Cloud and DevOps | Master’s in Data Science based in India. AI stack includes claude code, claude, and github copilot. Explore public work links including github."
image: "https://tal.club/meet/kartik-b-bhargav/share-image"
---

# Kartik B Bhargav on Tal

## Headline

Senior Analytics Consultant @ Quantzig \| Azure Certified \| MLOps, Cloud and DevOps \| Master’s in Data Science

## About

As a Senior Software Engineer at Vanderlande, I focus on enhancing operational awareness for airport stakeholders through our VIA Insights product\. By leveraging advanced analytics and real\-time data visualization within Splunk dashboards, our team empowers users to monitor key performance indicators of baggage handling systems\. My work integrates data from Oracle databases with Splunk to deliver meaningful insights that improve airport operations\. With over three years of experience in software engineering, I bring expertise in Azure cloud technologies and DevOps practices, refined through previous roles at Rabobank and TomTom\. My commitment to fostering efficient, data\-driven solutions aligns with Vanderlande's mission to optimize operational performance for global airport systems\.

## Location

India

## AI stack

claude code

claude

github copilot

claudecode

Google ADK

JetBrains AI Assistant

## Work experience

Senior Analytics Consultant at Quantzig, Sep 2025 \- Present · 1 yr 1 mo, Bengaluru, Karnataka, India

Senior Software Engineer at Vanderlande, Apr 2024 \- Apr 2025 · 1 yr 1 mo, Veghel, North Brabant, Netherlands, I am currently working in the Operational Awareness—Airports team within the Digital Service Factory group in Vanderlande\. Our team develops and maintains a product called Vanderlande Industries Airports \(VIA\) Insights\. VIA Insights is a set of dashboards, alerts and lookups developed in Splunk and deployed across various customer sites \(airports\)\. The stakeholders can visualize various KPIs and metrics and can gain meaningful insights regarding the baggage handling systems that are present in the sites\. The data to be visualized is collected in Oracle Database and it is forwarded to Splunk, where extensive computing is performed\. The results of the computation are displayed in Splunk dashboards\. Our team also deploys and maintains the entire infrastructure and data pipelines, that are required for the deployment of VIA Insights\. These are some of my key contributions to the team as a Senior Software Engineer: 1\. Automated the Azure IoTEdge deployment using Python which reduced the deployment time by upto 80%\. 2\. Setup Bitbucket repositories and Bamboo build plans for 5 customer sites, streamlining the CI/CD workflows within the team\. 3\. Deployed the data pipeline and cloud infrastructure of the Vanderlande Industries Airports \(VIA\) Insights on Azure cloud for 5 customer sites\. 4\. Identified KPIs, wrote queries in Splunk and developed a dashboard from scratch for the John F Kennedy Airport\. 5\. Implemented a shell script as a cronjob to detect and restart the unhealthy Docker containers in the development server\. 6\. Wrote a script in Python to notify the developers about the expiring certificates in Azure Key Vault\.

Software and DevOps Engineer at Rabobank, Dec 2022 \- Dec 2023 · 1 y, Utrecht, Netherlands, I was working in the "Category" squad which comes under the Tribe Data and Analytics\. Our team developed and maintained a tool called Category Engine which can categorizes transactions and assigned meaningful labels to them\. 1\. Implemented several Azure durable functions required to categorize transactions in real time streaming setup using Python\. 2\. Implemented the core orchestrator logic to categorize real time transactions using Python in Azure Cloud\. 3\. Implemented CI/CD Pipeline to run unit tests and integration tests and build and deploy the source code in Azure DevOps\. 4\. Developed the architecture of the app with Bicep and deployed the app to Azure Cloud via Azure DevOps pipelines\. 5\. Implemented Kafka Consumer to consume transactions in real time and assigned categorized these transactions using the Transaction Categorization Engine and then I implemented Kafka Producer to produce the categorized transactions to a Kafka topic\. 6\. Added a custom logging module to Transaction Categorization Engine and also added feature to send logs to Azure Application Insights using Python\. 7\. Created a dashboard in Azure Application Insights using KQL to monitor the operational metrics of the Transaction Categorization Engine\. 8\. Set up a local testing environment to test the entire architecture of the transaction categorization engine using Docker and PostgreSQL, which in turn sped up the development process\. 9\. Performed Exploratory Data Analysis using Databricks to resolve customer facing issues\. 10\. Manually labelled transactions that serves as a ground truth for the transaction engine\.

Data Software Engineer at TomTom, Nov 2021 \- Nov 2022 · 1 y, Eindhoven, North Brabant, Netherlands, 1\. Improved the speed of the map comparison tool by 300% by configuring the code to run in a Pypy environment\. 2\. Performed Exploratory Data Analysis of various map datasets using Python, Geopandas and QGIS 3\. Implemented the functionality to visualize the various map sources used in test scenarios using Matplotlib\. 4\. Increased the code coverage of the map comparison tool by adding new unit test cases using Pytest 5\. Performed benchmarking and evaluated the behavior of the map comparison tool for different noise levels using Python and numpy\.

Graduate Thesis Intern at TNO, May 2021 \- Nov 2021 · 6 mo, Helmond, North Brabant, Netherlands, My graduation thesis is titled "Remaining useful life prediction of lithium\-ion batteries using machine learning"\. As part of this, I conducted exploratory data analysis and extracted relevant features from the battery degradation dataset by TNO\. Finally, I implemented Support Vector Regression, Long Short Term Memory network and Instance based Learning models to predict the Remaining Useful Life \(RUL\) of Lithium\-ion batteries in electric vehicles\.

## Education

Master's degree, Data Science in Engineering, Eindhoven University of Technology

Bachelor of Engineering \- BE, Electrical, Electronics and Communications Engineering, SJCE, Mysore, India

## Public links

- github: https://github\.com/bhargavkartik

[View the original profile](https://tal.club/meet/kartik-b-bhargav)

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