Anshuman Mandal

7765828832 · anshuman.mandal.01@gmail.com Resume

Software Engineer with hands-on experience in building high-performance, concurrent systems and scalable microservices using Go, gRPC, Redis, and PostgreSQL. Skilled in designing distributed systems, real-time data pipelines, and ETL workflows with a focus on reliability, observability, and efficiency. Experienced in data engineering with Azure Data Factory, Databricks, and Delta Lake, applying advanced analytics and automation to optimize processes. Passionate about leveraging concurrency, distributed architectures, and data-driven approaches to solve complex real-world problems and deliver impactful solutions.

Experience

Software Engineer

Celebal Technologies | Jaipur

URL Shortening Service:

  • Built a scalable microservices-based URL shortening service using Go, gRPC, Redis, and PostgreSQL, adhering to CSP principles for safe concurrency.
  • Achieved 99.99% SLA with 1M+ daily redirects and <50ms average redirect latency via parallel replica resolution using goroutines and select.
  • Designed isolated services for shortening, redirection, and analytics using Go channels to avoid shared memory and race conditions.
  • Built an asynchronous analytics pipeline (fan-out/fan-in) to process clickstream data (geo-IP, user-agent) with minimal I/O blocking.
  • Implemented asynchronous structured logging using channels, improving observability and debugging efficiency by 40%.
  • Enforced Redis-based rate limits per IP/user using TTL and sliding window algorithms.
  • Applied traceable error propagation; integrated Prometheus/Grafana for metrics and OpenTelemetry for tracing; maintained <300ms shortening latency during peak load with <2s fault recovery using auto-healing and cached fallbacks.

Request Processing Platform:

  • Implemented a high-performance monolithic request processing system in Go using goroutines, channels, and fan-out/fan-in, enabling multi-client job submission and parallel task execution.
  • Applied the Ward Pattern with heartbeat channels and timeouts to monitor and auto-respawn worker goroutines, ensuring system resilience.
  • Leveraged fan-out/fan-in with a dynamic worker pool and buffered channels for scalable request queuing and efficient task allocation.
  • Enforced Redis-based per-client rate limiting (100 requests/min) to prevent overload and abuse.
  • Performance: Handled 500+ concurrent requests per minute with <250ms response time; Latency: improved task processing time by 40% using pipelined goroutine architecture; Reliability: achieved 99.98% uptime under simulated peak load conditions.
November 2024 - Present

Software Engineer

Ernst & Young | Bengaluru
  • Built scalable ETL pipelines using Azure Data Factory and Databricks, migrating data from 17+ SQL Server systems into ADLS Gen2 following Medallion Architecture. Re-engineered 130+ stored procedures into PySpark with SCD Type 1 & 2, window functions, Delta Lake MERGE, partitioning, and watermarking.
  • Enabled governance and optimization via Unity Catalog for schema/lineage tracking, Spark UI for performance tuning, Delta time travel for auditability, MoM reconciliation for data validation, and vacuuming to reduce storage footprint.
June 2023 – August 2024

Software Engineer Intern

Skyserve.ai | Bengaluru

Built an Earth Engine Scraper using Geemap to retrieve Google Earth Engine Maps Dataset for Urban Monitoring.

Worked on OS and network modules to optimize system performance and network compatibility.

Conducted compatibility analysis on libraries for satellite image processing to ensure seamless data integration and processing.

June 2022 - August 2022

My Projects

Here are some of the key projects I've worked on, showcasing my skills in data analysis, machine learning, and web development.

Logistics Performance Analysis and Delay Prediction and Consumer Segmentation

Analyzed logistics data to identify key factors affecting delivery performance followed by Consumer Segmentation using algorithms (Random Forest, Gradient Boosting). Built machine learning models to classify orders, achieving 69.82% test accuracy. Developed insights to reduce delays and boost profitability, visualized via a Power BI dashboard.

GitHub Link PDF Link

Disaster Detection and Classification Using Text, Images, and Real-Time Twitter Scraping

The project uses text and image classification models to detect disaster-related content, achieving up to 81% accuracy for text and 80% for images (VGGNet). A deployed website allows classification on user-uploaded data or real-time Twitter posts using a live scraper for disaster detection.

GitHub Link PDF Link

Document Image Denoising Using Convolutional Autoencoders

Used a Convolutional Autoencoder to denoise document images while preserving details. The autoencoder learns key features through unsupervised learning, effectively removing noise. The approach shows promising results for tasks like document restoration and OCR preprocessing.

GitHub Link PDF Link

Machine Translation Using Sequence to Sequence Transformer

Built an Encoder-Decoder Transformer for English-Hindi translation using TensorFlow. Preprocessed Hindi-English corpus with tokenization, padding, and special tokens. Achieved a Bilingual Evaluation Understudy Score of 0.8 with stable training convergence.

GitHub Link PDF Link

Education

PES University

Bachelor of Technology
Computer Science and Engineering

SGPA: 9.00

2019 - 2023

International Public School, Kanke Road, Ranchi

12th Grade

Percentage: 75.8% (Physics, Chemistry, Mathematics)

2017 - 2019

St. Thomas School, Dhurwa, Ranchi

10th Grade

Percentage: 93.5%

2005 - 2017

My Skills

Software Engineering

  • Languages: Go, Python, SQL, C++, C#
  • Frameworks and Libraries: Gin, gRPC, REST, JSON, Protocol Buffers, Kafka
  • Databases and Storage: PostgreSQL, Redis, SQL Server, Delta Lake, Parquet
  • Concurrency, Microservices, API Development, Big Data, Distributed Systems
  • Data Engineering, Real Time Streaming, Task Queues, Fan-out/Fan-in Patterns

Cloud

  • Cloud Platforms & Tools: Azure Data Factory, Databricks, ADLS Gen2, Azure Blob Storage, Docker, Kubernetes
  • Distributed Systems & Cloud Practices: CSP, Microservices, Data Pipelines, Event-driven Architecture
  • Observability & Monitoring: Prometheus, Grafana, OpenTelemetry
  • Data Warehousing, ETL, Delta Time Travel, Vacuuming, MoM Reconciliation
  • Performance Tuning, Auto-healing, Fault Recovery, Rate Limiting

Analytics Projects

  • Data Visualization
  • Exploratory Data Analysis (EDA)
  • Hypothesis Testing
  • Clustering
  • Dimensionality Reduction
  • Anomaly Detection
  • Predictive Modeling
  • Time Series Forecasting
  • Feature Engineering
  • Regression
  • SQL
  • Database Management Systems

Tools

  • Programming Languages: Go, Python, SQL, C++, C#
  • Frameworks and Libraries: Gin, gRPC, REST, JSON, Protocol Buffers, Kafka, PyTorch, TensorFlow, Keras, scikit-learn, OpenCV, Matplotlib, Seaborn, NumPy
  • Databases and Storage: PostgreSQL, Redis, SQL Server, ADLS Gen2, Delta Lake, Parquet, Azure Blob Storage, MySQL, BigQuery
  • Distributed Systems and Cloud: Microservices, CSP, Data Factory, Databricks, Docker, Kubernetes, Spark, Hadoop, Azure
  • Observability and Monitoring: Prometheus, Grafana, OpenTelemetry
  • Deployment Tools: Streamlit, Flask, Power BI, Tableau, MS Excel

Interests

Apart from being a Data Analyst, I love fitness and running. When outdoors, I enjoy trekking and exploring nature.

When indoors, I Netflix and chill and engage in competitive board games .