Data engineering
Data modelling, ingestion, transformation and orchestration built for dependable analytics.
DATA ENGINEERING · CLOUD · FINTECH
Turning complex data into
clarity that
moves business.
I’m Aathi — an Azure Data Engineer focused on resilient data platforms, Data Vault 2.0 architecture and high-impact engineering for financial services.
DATA↗IN MOTION
INGEST
MODEL
DELIVER
FIG 01 / ENGINEERED FOR SCALE
BASED IN GERMANYPYTHON / SQL / AZURE / SPARKSCROLL TO EXPLORE ↓
01 / THE PERSON BEHIND THE PIPELINES
Engineering the
foundation of better decisions.
With around four years of experience in FinTech, I build and optimise modern data solutions — from reliable ingestion and transformation to governed, scalable data platforms.
My work combines hands-on engineering with a strong focus on performance, data quality, consistency and regulatory-aware delivery. I enjoy turning demanding business requirements into systems teams can trust.
01 — RELIABILITY02 — CLARITY03 — SCALE
02 / WHAT I BRING
Tools for the
big picture.
A practical toolkit for designing, building and operating modern data ecosystems.
Cloud & platform
Cloud-native data platforms, distributed processing and automated delivery workflows.
Data foundations
Enterprise data storage, migration patterns and dimensional modelling for trusted information.
03 / EXPERIENCE & EDUCATION
Built through
experience.
A progression from hands-on delivery to engineering business-critical data platforms.
PROFESSIONAL EXPERIENCE
MAR 2024 — MAR 2026
Software Developer / Data Engineer
BNP Paribas CIB
- Built and operated scalable Data Vault 2.0 warehouse solutions supporting investment-banking workflows.
- Orchestrated data pipelines with Apache Airflow and transformations with dbt.
- Improved processing performance by 30% through Azure Data Factory and Databricks optimisation.
AUG 2022 — MAR 2024
Software Developer
BNP Paribas CIB
- Progressed from intern to Data Engineer, taking ownership of business-critical pipelines and core data platforms.
- Created an automated SSIS / SQL Server to Oracle migration framework using Data Vault 2.0, reducing manual effort by 65%.
FEB 2022 — AUG 2022
Data Engineering Intern
BNP Paribas CIB
- Built and optimised data pipelines for upstream and downstream processes.
- Automated deployment processes with Python, reducing manual effort by 97%; supported L3 troubleshooting and testing.
EDUCATION
2020 — 2022
Master of Computer Applications
SASTRA University
Rank holder · CGPA 8.08. Academic work included patient-response sentiment analysis using machine-learning models; supported web development and React learning.
2017 — 2020
Bachelor’s degree in Physics
Manonmaniam Sundaranar University
Graduated with distinction · CGPA 7.93. Built an IoT smart-home prototype integrated with Google Assistant; participated as a student helper at NASA Space Expo.
04 / SELECTED PROJECTS
Work with
real-world impact.
A selection of data engineering, migration and product projects. Some engagements are described at a high level to respect confidentiality.
01 / DATA PLATFORMAZURE ECOSYSTEM
ADF→NiFi→Databricks→ADLS
DATA PLATFORM · CLIENT PROJECT
Sneka Advertising
Designed a cloud data workflow ingesting and orchestrating data with Azure Data Factory and Apache NiFi, transforming it in Databricks / Microsoft Fabric and storing it in Azure Data Lake Storage. Azure DevOps supported CI/CD.
02 / DATA MODELLINGPYTHON FRAMEWORK
DV 2.0
SOURCE → CLEAN → LOAD
DATA VAULT · ORACLE
COMPALA
Extracted dimensional data from source files, cleaned and transformed it to business requirements, then loaded it into Oracle through a Python-based Data Vault ingestion framework.
03 / DATA MIGRATIONCROSS-PLATFORM
SQL
SERVER
↠SERVER
ORACLE
+ S3
+ S3
RECONCILIATION / ALERTING
MIGRATION · AUTOMATION
COMPASS
Migrated client-contribution data from SQL Server to Oracle SQL and Amazon S3 using a Python framework, with reconciliation checks and email alerts for discrepancies.
04 / MACHINE LEARNINGNLP
“data
has a voice.”
has a voice.”
Naive BayesGradient BoostingText analytics
ACADEMIC · NLP
Sentiment analysis
Explored sentiment in patient responses related to medication adherence, applying machine-learning models including Naive Bayes and Gradient Boosting to identify patterns.
05 / WEB DEVELOPMENTFULL STACK
● ● ●
Publish ideas.
Build conversations.
WEB APP · PERSONAL PROJECT
Blog website
Built a Node.js-based blog website with post creation functionality and a straightforward publishing experience.
05 / ENGINEERING OUTCOMES
Better systems.
Measurable results.
+30%
data processing performance improvement
−65%
manual effort through migration automation
−97%
manual deployment effort through Python automation
Figures reflect outcomes reported for specific projects and workflows.
06 / LET’S CONNECT
Have a data
challenge?
Let’s talk about building reliable data platforms, improving pipelines or turning complex information into something useful.