95%
Deduplication accuracy in AI-assisted extraction
Data Engineer · Verition Fund · NYC
Building reliable data systems for high-stakes decisions.
I'm Ahsan, a Data Engineer at Verition Fund Management, a multi-strategy hedge fund, where I build data pipelines, platform infrastructure, and production-grade data systems.
Previously: Accenture, NYC Department of Buildings, Fujitsu, WebMD, and ASML.
Focused on finance-grade data platforms with strong reliability, data governance, and trust.
Impact
What I deliver
80%
Reduction in manual data processing
50%
Faster processing in modernized data quality workflows
40%
Increase in enterprise data quality accuracy
How I support data teams
- Build reliable data ingestion pipelines with validation and retry controls.
- Improve trust in critical datasets through quality checks and observability patterns.
- Deliver cleaner production datasets faster for research and decision workflows.
Experience
Professional experience
Verition Fund Management
Data Engineer
- Engineering data pipelines and platform infrastructure to support the firm's data operations.
- Designing end-to-end data sourcing and delivery workflows for internal teams.
- Building ETL processes with Python, cloud services, scheduling, and monitoring.
- Implementing data quality and validation frameworks across multiple data sources.
- Normalizing and standardizing datasets across asset classes for firm-wide consumption.
Accenture
Senior Data Engineer
- Built a scalable web-scraping platform using Python, Playwright, ScraperAPI, and BeautifulSoup to ingest structured and unstructured alt-data across multiple sources.
- Developed AI-assisted extraction pipelines (Gemini + fuzzy matching), enabling 95% deduplication accuracy and reducing manual processing by 80%.
- Designed cloud-hosted ETL workflows on GCP Compute Engine with cron scheduling, REST integrations, retries, monitoring, logging, and automated historical storage.
- Implemented validation and schema enforcement to maintain accuracy, freshness, and consistency across thousands of daily records.
- Architected centralized NocoDB master and active tables with lifecycle automation, entitlement controls, and real-time synchronization.
Data Engineer
- Led design and deployment of a telecom data quality framework using AWS Deequ, Databricks, Spark, Scala, Python, and SQL.
- Automated 30+ data quality checks, reducing validation time by 40% and improving processing speed by 50%.
- Developed scalable ETL systems across AWS and Databricks for reliable high-volume production pipelines.
NYC Department of Buildings
Machine Learning Engineer
- Designed and developed supervised ML models for the Analytics and Data Science Unit to predict high-risk buildings from historical construction injury data.
- Engineered multi-year public datasets and built robust train/validate/test workflows with upsampling and hyperparameter tuning.
- Evaluated Gradient Boosting, Logistic Regression, Neural Networks, K-Nearest Neighbors, and SVM to identify the most reliable model for inspection prioritization.
- Implemented model analysis and visualization pipelines in Jupyter using NumPy, pandas, matplotlib, scikit-learn, and GeoPandas.
- Improved risk-based inspection planning and contributed to a 35% reduction in construction incidents.
Fujitsu Network Communications
Software Engineering Intern
- Designed and developed a cloud-based web application for the Software Business Unit (SWBU) to generate XML, JSON, and XLSX outputs for Virtuora Planning and Design workflows.
- Built a user-friendly interface to create and download multi-format files in a single streamlined workflow.
- Eliminated recurring format errors and reduced average manual build time from 25 hours to 30 minutes.
- Project was adopted by a major telecom client and generated approximately $100K in revenue impact.
- Tech stack: HTML, CSS, JavaScript, Java 8, Oracle DB, jQuery, jQuery UI, AJAX, and Bootstrap.
WebMD
Software Engineering Intern
- Developed and contributed to multiple full-stack applications for WebMD's Consumer Runtime platform.
- Designed and implemented a Page Performance Testing Dashboard to streamline test creation, tracking, and stakeholder approvals before production deployment.
- Automated route creation and route validation workflows to remove manual QA steps and improve developer throughput.
- Implemented regex-based URL validation and PostgreSQL-backed search functionality to improve routing reliability.
- Designed user interfaces using MVC design principles for maintainable internal tooling.
ASML
Software Engineering Intern
- Delivered two automation tools for the Reticle Stage SDEV team, including a Linux/Python app for compatibility agreement generation.
- Reduced compatibility agreement generation from 8 hours to 10 seconds, eliminating manual errors and enabling more frequent execution.
- Built a Python utility for rapid software build infrastructure navigation, removing the need to manually access scope files.
- Collaborated on a joint automation initiative to streamline GTA (Google Test Assistant) input collection workflows.
- Redesigned algorithm logic to extract testable functions from C/C++ source code, automating a repetitive unit-testing preparation step.
Skills
Technical stack
Tools and platforms I use most in production data engineering work.
Programming & Core Engineering
Python
SQL
Scala
Java
Bash
Git
Cloud & Infrastructure
S3
Lambda
ECS / ECR
EventBridge
Glue
Snowflake
Databricks
Data Orchestration & DevOps
Apache Airflow
Docker
Kubernetes
CI/CD
Terraform
Data Modeling & Architecture
Star Schema
Snowflake Schema
Schema Design (OLTP vs OLAP)
Data Partitioning Strategies
Data Quality & Governance
Deequ
Data Validation Rules
Schema Enforcement
Metadata Management
Data Lineage
Data Collection & Automation
Scrapy
BeautifulSoup
Playwright
Selenium
ScraperAPI
Certification