Aditya Menon | AI & Data Engineer

DATAENGINEER

2024 - Present


            
            
            
            
            
            
            
            
            
            
            
            
import requests
from datetime import datetime, timezone
import pandas as pd
from google.cloud import bigquery

def ingest_properties():
    # Extract: pull property listings from external API
    resp = requests.get("https://api.example.com/properties", timeout=30)
    resp.raise_for_status()
    data = resp.json()
    df = pd.json_normalize(data)

    # Transform: rename, types, metadata
    df.rename(columns={"priceUsd": "price_usd"}, inplace=True)
    df["ingested_at"] = datetime.now(tz=timezone.utc).isoformat()
    df["price_usd"] = pd.to_numeric(df["price_usd"], errors="coerce")

    # Load: append to BigQuery partitioned table
    client = bigquery.Client()
    table_id = "my_project.analytics.properties_raw"
    job = client.load_table_from_dataframe(
        df,
        table_id,
        job_config=bigquery.LoadJobConfig(write_disposition="WRITE_APPEND"),
    )
    job.result()
    return "loaded " + str(len(df)) + " rows"

ABOUT

I’m a Computer Science student at Michigan State University focused on data engineering and analytics at the intersection of finance, real estate, and technology. My work centers on building scalable data pipelines, backend systems, and analytics platforms that transform complex, real-world data into reliable inputs for decision-making. I’ve worked across the full data lifecycle from ingestion and transformation to modeling and analytic on financial, operational, and large-scale datasets. As the Backend Software Engineering Lead for an industry-sponsored capstone project with Magna International, I led the design of backend services for a VR platform integrating natural language interfaces with data-driven CAD workflows, focusing on system architecture, APIs, and reliability. Previously, at Lansing Board of Water & Light, I automated data workflows and built analytics dashboards to improve operational visibility and decision-making. Beyond industry and academic roles, I’ve built CDC-style analytics pipelines, backfill-safe incremental systems, and property analytics workflows spanning global real-estate data. I’m particularly interested in how data engineering, machine learning, and software systems enable smarter financial and real-estate intelligence. My goal is to build reliable data systems that power high-impact decisions in fintech, proptech, and data-driven technology companies.

EXPERIENCE

Backend Software Engineering Lead (Capstone) · Magna International

2025 – Present

Led backend architecture and API design for a VR platform integrating natural-language interfaces with CAD workflows, focusing on reliability, data handling, and scalable services.

Analytics Engineer · Lansing Board of Water & Light

Internship • May 2025 – Aug 2025

Automated and reconciled operational data across enterprise systems using Python and Power Platform tools, improving reporting reliability and reducing manual correction effort.

Data Engineer · ADNOC Distribution

Internship • May 2024 – Aug 2024

Built analytics-ready tables and data models on Oracle Cloud data warehouses, developed Python-based data validation workflows, and delivered Power BI dashboards enabling cross-functional teams to monitor operational and performance KPIs.

Portfolio

Real Estate Market Intelligence Pipeline

Jul 2025 - Dec 2025

Developed a scalable ELT system for property data, combining batch and streaming ingestion, automated quality testing, and cost-efficient warehouse design in BigQuery.

View Project

Change Data Capture (CDC) Pipeline

Nov 2025 - Present

Built a CDC pipeline to stream row-level changes from OLTP systems into the warehouse, enabling near-real-time analytics with idempotent upserts and schema-evolution handling.

View Project

Backfill‑Safe Incremental Ingestion

Dec 2025 - Present

Designed an incremental loader with bookmark/watermark checkpoints, windowed backfills, and deduplication to ensure accuracy while minimizing compute costs.

View Project

Algorithmic Trading System

May 2024 - July 2024

The project employs machine learning techniques, particularly unsupervised learning, to extract insights from financial data, such as patterns and relationships that are not predefined.

View Project

INTERESTS

Playing Chess : This is my mental gym. I love playing fast paced chess game as it improves my sharpness and time management.
Playing Soccer : I love playing soccer as it helps me stay fit and active. I also love to watch soccer games.
ATV : I love to go on ATV rides in the mountains. It is a great way to enjoy nature and get some exercise.
Traveling : I love to travel and explore new places. Currently, been to over 20 countries (majorly in Europe and Asia).
Gymming : I love to gym as it helps me stay fit and active. I also love to watch gym videos and learn new exercises.
Surfing : I love to surf as it is a great way to enjoy nature and get some exercise.

Chess - My Mental Gym

ATV - Off-road Adventures

Traveling - Global Explorer

Fitness - Staying Active

Surfing - Catching Waves