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ManojGowda27/README.md
Superbad Sup

I'm Manoj Gowda 👋

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📖 About Me

I am an Analytics Engineer specializing in bridging the gap between raw data and decision-making. My work focuses on building trusted analytics layers and scalable ELT pipelines that actually withstand production environments.

Unlike standard data dumps, I focus on the reliability of the ecosystem. Whether it's optimizing billing analytics for finance teams or refactoring ingestion logic to prevent memory failures, I treat data models as products.

  • 🔭 Currently working on: Advanced dbt modeling and establishing data governance frameworks.
  • 💡 Interested in: Distributed systems, Data Lakehouse architectures, and identifying "silent failures" in data pipelines.
  • 🎓 Alumni: MS in Computer Science, University of Massachusetts Lowell.

💼 Professional Experience Highlights

Role Impact
Billing Data Analyst @ Lob (Currently Working) Reduced report generation time by 30% by unifying pricing/tracking data into trusted dbt models. Automated validation tests to ensure strict financial governance.
Data Analyst @ ElevateMe Engineered statistical models for customer segmentation and built real-time ETL pipelines using Python and Spark.
Data Analyst @ InfoTrack Optimized SQL queries for extraction, boosting supply chain efficiency by 15%.

🛠️ Tech Stack & Tools

I choose tools based on the problem at hand—reliability for finance, throughput for streaming.

Analytics Engineering & Warehousing

Orchestration & Transformation

Languages & Visualization

🚀 The Story Behind The Code (Featured Projects)

A showcase of not just "what" I built, but "why" I built it that way.

Featured Projects

1. Reddit Data Lakehouse Pipeline

The Problem: I needed to handle high-volume data ingestion without crashing worker nodes due to memory constraints.

The Evolution:

  • Iteration 1: Basic list-based loading (O(N) memory complexity) which caused OOM failures.
  • Iteration 2: Refactored to Python Generators (lazy evaluation), reducing memory complexity to O(1).
  • Architecture: Implemented an atomic extract-load pattern on Apache Airflow to resolve distributed race conditions across nodes.

Outcome: A stable Star Schema in Redshift with automated schema inference via AWS Glue.

View Repo

2. Realtime Data Streaming Architecture

The Problem: The legacy TCP prototype lacked backpressure handling, meaning any consumer downtime or network lag resulted in permanent data loss.

The Evolution:

  • Resilience: Replaced legacy TCP with Apache Kafka & Spark Structured Streaming to guarantee zero data loss and replayability.
  • Intelligence: Integrated OpenAI GPT-3.5 for real-time sentiment analysis, implementing selective filtering to minimize API costs.
  • Infrastructure: Deployed a fully containerized microservices ecosystem (Zookeeper, Kafka, Spark) using Docker Compose.

Outcome: A fault-tolerant, real-time pipeline capable of high-throughput ingestion, stream processing, and indexed search.

View Repo

"Good data engineering is invisible. If I do my job right, you simply trust the numbers."

Let's connect!
LinkedIn • Email

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  1. Realtime_Data_Streaming Realtime_Data_Streaming Public

    A fault-tolerant, real-time data engineering pipeline evolved from a TCP prototype to a robust Kafka architecture. Features high-throughput ingestion with Apache Kafka, stream processing with Spark…

    Python

  2. reddit-aws-lakehouse reddit-aws-lakehouse Public

    This project is a scalable, fault-tolerant Data Engineering pipeline designed to ingest, transform, and analyze Reddit data. It demonstrates a move away from simple scripting towards software engin…

    Python

  3. Nashville-Housing-Data Nashville-Housing-Data Public

    This repository contains SQL queries used for data preprocessing and transformation on the Nashville Housing dataset. These queries aim to standardize data formats, populate missing values, break d…

  4. ELT-Data-Pipeline ELT-Data-Pipeline Public

    This project implements a data pipeline using industry-standard tools such as dbt, Snowflake, and Airflow. It facilitates the extraction, loading, and transformation (ELT) process of data, enabling…

    Python 1 1

  5. citibike_user_prediction citibike_user_prediction Public

    This project aims to predict the user type of a bike-sharing service based on trip duration. Additionally, the project explores whether a customer could be a potential subscriber by analyzing the f…

    Jupyter Notebook

  6. Movies-Correlation-Using-Python Movies-Correlation-Using-Python Public

    This repository contains Python code for performing data analysis on a movie dataset. The analysis includes visualizations and statistical insights derived from the data.

    Jupyter Notebook