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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<!-- Primary SEO -->
<title>Ashutosh Kumar | Data Engineer — PySpark, AWS, GCP, Databricks</title>
<meta name="description"
content="Ashutosh Kumar is a Data Engineer with 3+ years of experience building scalable ETL pipelines, cloud data platforms, and distributed systems across AWS, Azure, and GCP. Databricks Certified.">
<meta name="keywords"
content="Data Engineer, PySpark, Apache Spark, ETL Pipeline, AWS Data Engineer, GCP BigQuery, Databricks, Airflow, Hadoop, Big Data, Cloud Data Platform, Data Engineering Portfolio, Python Data Engineer, Azure Data Engineer">
<meta name="author" content="Ashutosh Kumar">
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<meta property="og:url" content="https://ashutro.github.io/">
<meta property="og:title" content="Ashutosh Kumar | Data Engineer — PySpark, AWS, GCP, Databricks">
<meta property="og:description"
content="Data Engineer with 3+ years of experience in PySpark, AWS, GCP, Azure, Databricks. Building scalable ETL pipelines and cloud data platforms.">
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<!-- Twitter Card -->
<meta name="twitter:card" content="summary_large_image">
<meta name="twitter:title" content="Ashutosh Kumar | Data Engineer — PySpark, AWS, GCP, Databricks">
<meta name="twitter:description"
content="Data Engineer with 3+ years of experience in PySpark, AWS, GCP, Azure, Databricks. Building scalable ETL pipelines and cloud data platforms.">
<meta name="twitter:image" content="https://ashutro.github.io/profile.jpg">
<!-- JSON-LD Structured Data: Person -->
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{
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"Apache Airflow", "AWS", "Google Cloud Platform", "Microsoft Azure",
"Databricks", "BigQuery", "Hadoop", "Python", "SQL", "Data Modeling"
],
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<!-- Fonts (preconnect for speed) -->
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link
href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600;800&family=Fira+Code:wght@400;500&display=swap"
rel="stylesheet">
<link rel="stylesheet" href="style.css">
</head>
<body>
<canvas id="bg-canvas" class="background-elements" aria-hidden="true"></canvas>
<nav class="navbar" aria-label="Main navigation">
<div class="logo" aria-label="Ashutosh Kumar Portfolio">Ashutosh Kumar</div>
<div class="menu-toggle" id="mobile-menu" aria-label="Toggle navigation menu" aria-expanded="false"
role="button" tabindex="0">
<span class="bar"></span>
<span class="bar"></span>
<span class="bar"></span>
</div>
<ul class="nav-links" role="list">
<li><a href="#about">About</a></li>
<li><a href="#skills">Skills</a></li>
<li><a href="#experience">Experience</a></li>
<li><a href="#projects">Projects</a></li>
<li><a href="#certifications">Certifications</a></li>
<li><a href="#education">Education</a></li>
<li><a href="#contact" class="btn-nav">Contact</a></li>
</ul>
</nav>
<main id="main-content" aria-label="Portfolio content">
<section id="hero" class="hero-section section">
<div class="hero-content">
<div class="hero-header-row">
<div class="hero-profile-container">
<img src="./profile.jpg" alt="Ashutosh Kumar" class="hero-profile-img" width="150" height="150"
loading="lazy">
</div>
<h1 class="hero-title">
<span class="greeting">Hi, I'm</span>
<span class="name">Ashutosh Kumar</span>
</h1>
</div>
<h2 class="title"><span class="gradient-text">Data Engineer</span> | PySpark | AWS | GCP | Databricks
</h2>
<p class="tagline" id="typing-text"></p>
<div class="stats-container">
<div class="stat-item">
<div class="stat-value">
<span class="counter" data-target="3">0</span><span class="plus-icon">+</span>
</div>
<p>Years Experience</p>
</div>
<div class="stat-item">
<div class="stat-value">
<span class="counter" data-target="4">0</span>
</div>
<p>Cloud Certifications</p>
</div>
</div>
<div class="cta-group">
<a href="#projects" class="btn primary-btn">View Projects</a>
<a href="https://drive.google.com/file/d/1rGG8yaeLQw2uBXpeDJPjEASb9xENMLV2/view?usp=sharing"
target="_blank" rel="noopener noreferrer" class="btn secondary-btn">View Resume</a>
<a href="#contact" class="btn secondary-btn">Contact Me</a>
</div>
</div>
</section>
<section id="about" class="section fade-in">
<h2 class="section-title"><span>01.</span> About Me</h2>
<div class="about-content">
<div class="about-text">
<p>I am a Data Engineer with 3+ years of experience building scalable data platforms and
high-performance ETL/ELT pipelines across modern cloud and big data ecosystems. My work focuses
on designing reliable data systems that process large-scale datasets and power analytics,
reporting, and intelligent data-driven applications.</p>
<p>I specialize in distributed data processing and pipeline engineering using technologies such as
<span class="highlight">Python</span>, <span class="highlight">PySpark</span>, <span
class="highlight">Apache Spark</span>, <span class="highlight">Airflow</span>, <span
class="highlight">Hadoop</span>, and <span class="highlight">SQL</span>. I have hands-on
experience building production pipelines that process millions of records, optimizing Spark
workloads, and designing data architectures that improve performance, scalability, and data
reliability.
</p>
<p>My expertise spans cloud data engineering across <span class="highlight">AWS</span>, <span
class="highlight">Microsoft Azure</span>, and <span class="highlight">Google Cloud</span>,
along with modern data platforms like <span class="highlight">Databricks</span> and cloud data
warehouses. I work across the full data lifecycle—from data ingestion and transformation to data
modeling, orchestration, and analytics enablement.</p>
<p>I also work closely with <span class="highlight">AI and machine learning</span> data workflows,
building data pipelines that prepare large datasets for ML models, feature engineering, and
AI-driven analytics systems. My focus is on enabling reliable and scalable data foundations that
support modern AI and data science platforms.</p>
<p>With a strong foundation in distributed computing, data modeling, and pipeline automation, I
design production-grade data solutions that are scalable, maintainable, and cloud-ready.</p>
<p>I am also <span class="highlight">Databricks certified</span> and continuously explore modern
technologies in cloud data engineering, big data processing, and AI-enabled data platforms.</p>
</div>
</div>
</section>
<section id="skills" class="section fade-in">
<h2 class="section-title"><span>02.</span> Technical Arsenal</h2>
<div class="skills-grid">
<div class="skill-category">
<h3>Programming</h3>
<ul>
<li>Python</li>
<li>PySpark</li>
<li>Shell Scripting</li>
<li>Apache Spark</li>
<li>Airflow</li>
</ul>
</div>
<div class="skill-category">
<h3>Databases / Datawarehouse</h3>
<ul>
<li>Redshift</li>
<li>AlloyDB / PostgreSQL</li>
<li>BigQuery</li>
<li>CloudSQL</li>
<li>SQL Server</li>
</ul>
</div>
<div class="skill-category">
<h3>Data Modeling</h3>
<ul>
<li>Star / Snowflake Schemas</li>
<li>Dimensional Modeling</li>
<li>Fact & Dimension Tables</li>
</ul>
</div>
<div class="skill-category">
<h3>Cloud Technologies</h3>
<ul>
<li>GCP (BigQuery, CloudSQL, Dataflow)</li>
<li>AWS (S3, Glue, EMR, Redshift)</li>
<li>Azure (Synapse, Data Factory)</li>
<li>Databricks (Delta Lake)</li>
</ul>
</div>
<div class="skill-category">
<h3>Tools</h3>
<ul>
<li>Git & JIRA</li>
<li>Linux & Windows</li>
</ul>
</div>
</div>
</section>
<section id="experience" class="section fade-in">
<h2 class="section-title"><span>03.</span> Professional Experience</h2>
<div class="timeline">
<div class="timeline-item">
<h3 class="role">Big Data Engineer</h3>
<h4 class="company">Infosys India</h4>
<p class="date"><time datetime="2022">2022</time> – Present</p>
<div class="exp-category">
<h5>Data
Engineering & ETL Development</h5>
<ul>
<li>
Designed and optimized production-grade ETL pipelines using PySpark, Python, Apache
Airflow, and Hadoop, processing millions of records and improving processing efficiency
by 80%.</li>
<li>
Built scalable Spark transformations for large transactional datasets, reducing job
runtime and enabling faster data availability for analytics and reporting systems.</li>
<li>
Developed reusable data ingestion frameworks to standardize loading data from multiple
sources including files, APIs, and relational databases.</li>
<li>
Debugged and optimized production ETL pipelines, improving reliability and reducing
incident resolution time.</li>
</ul>
</div>
<div class="exp-category">
<h5>Workflow
Orchestration & Automation</h5>
<ul>
<li>
Enhanced critical Airflow-orchestrated workflows using Python and Spark optimizations
such as partitioning, caching, and parallel execution, improving pipeline performance by
80%.</li>
<li>
Automated ETL monitoring and health checks using Shell scripting and Linux cron jobs,
reducing manual intervention and improving SLA compliance by 20%.</li>
<li>
Implemented automated validation checks and monitoring alerts for pipeline failures and
performance issues.</li>
</ul>
</div>
<div class="exp-category">
<h5>Data Quality &
Data Modeling</h5>
<ul>
<li>
Implemented a data quality framework with validation checks, schema enforcement, and
anomaly detection, achieving 95%+ accuracy across transactional datasets and reporting
layers.</li>
<li>
Collaborated with analytics and business teams to design data models and curated
reporting layers supporting operational and executive dashboards.</li>
<li>
Developed optimized SQL queries and reporting datasets for faster analytics and improved
business insights.</li>
</ul>
</div>
<div class="exp-category">
<h5>Cloud &
Distributed Data Platforms</h5>
<ul>
<li>
Integrated Spark pipelines with cloud-based data platforms including AWS (S3, Redshift),
Azure Data Lake / Azure Databricks, and GCP BigQuery.</li>
<li>
Worked with Databricks notebooks and Spark clusters for distributed data processing and
ETL workload optimization.</li>
<li>
Explored GCP services such as BigQuery and CloudSQL to support cloud migration and
modern data warehouse architectures.</li>
<li>
Built data pipelines compatible with hybrid-cloud architectures combining on-premise and
cloud data platforms.</li>
</ul>
</div>
<div class="exp-category">
<h5>Global
Collaboration & Cross-Functional Delivery</h5>
<ul>
<li>
Collaborated with global stakeholders and engineering teams across US, EU, and APAC
regions, delivering data engineering solutions supporting international business
operations.</li>
<li>
Worked closely with data analysts, data scientists, product teams, and business
stakeholders to translate business requirements into scalable data pipelines and
reporting datasets.</li>
<li>
Participated in cross-functional agile teams, contributing to sprint planning,
production deployments, and incident resolution for critical data workflows.</li>
<li>
Supported global reporting and analytics platforms, ensuring reliable and timely data
availability for teams operating across multiple time zones.</li>
<li>
Coordinated with platform engineers, DevOps teams, and cloud architects to deploy and
optimize data pipelines across distributed environments.</li>
</ul>
</div>
</div>
</div>
</section>
<section id="projects" class="section fade-in">
<h2 class="section-title"><span>04.</span> Featured Projects</h2>
<div class="projects-container">
<div class="project-card">
<h3 class="project-title">Enterprise ETL Pipeline Optimization</h3>
<p><strong>Architecture:</strong> Designed and deployed a modular ETL pipeline using AWS Glue,
Spark, and Airflow, integrating multi-source data into a centralized Redshift warehouse.</p>
<p><strong>Impact:</strong> Achieved 40% faster load times and improved data quality by 95%,
enabling accurate and timely reporting for business teams.</p>
<div class="project-tech">
<span>AWS Glue</span>
<span>Apache Spark</span>
<span>Airflow</span>
<span>Redshift</span>
</div>
</div>
<div class="project-card">
<h3 class="project-title">Data Lakehouse for E-commerce Analytics</h3>
<p><strong>Architecture:</strong> Developed a data lakehouse on Databricks (Delta Lake) integrating
raw, cleaned, and curated zones.</p>
<p><strong>Impact:</strong> Automated ingestion from APIs and RDBMS, enabling unified analytics
across sales and user engagement data.</p>
<div class="project-tech">
<span>Databricks</span>
<span>Delta Lake</span>
<span>ETL</span>
<span>Python</span>
</div>
</div>
<div class="project-card">
<h3 class="project-title">dataforge</h3>
<p><strong>Architecture:</strong> A Docker-first data engineering platform wiring together Spark,
Hadoop (HDFS), Kafka + Zookeeper, Airflow, Postgres, MongoDB, and Jupyter using Docker Compose.
</p>
<div class="project-tech">
<span>Docker</span>
<span>Spark</span>
<span>Kafka</span>
<span>Airflow</span>
</div>
<a href="https://github.com/ashutro/dataforge" target="_blank" rel="noopener noreferrer"
class="github-link">
<svg viewBox="0 0 24 24" width="20" height="20" stroke="currentColor" stroke-width="2"
fill="none" stroke-linecap="round" stroke-linejoin="round">
<path
d="M9 19c-5 1.5-5-2.5-7-3m14 6v-3.87a3.37 3.37 0 0 0-.94-2.61c3.14-.35 6.44-1.54 6.44-7A5.44 5.44 0 0 0 20 4.77 5.07 5.07 0 0 0 19.91 1S18.73.65 16 2.48a13.38 13.38 0 0 0-7 0C6.27.65 5.09 1 5.09 1A5.07 5.07 0 0 0 5 4.77a5.44 5.44 0 0 0-1.5 3.78c0 5.42 3.3 6.61 6.44 7A3.37 3.37 0 0 0 9 18.13V22">
</path>
</svg>
View Repository
</a>
</div>
<div class="project-card">
<h3 class="project-title">Query-gpt-ollama</h3>
<p><strong>Architecture:</strong> Streamlit-based AI assistant translating natural language
questions into SQLite structural queries utilizing LLMs like Ollama (Mistral) or OpenAI GPT-4
via LangChain.</p>
<div class="project-tech">
<span>Python</span>
<span>LangChain</span>
<span>LLM</span>
<span>Streamlit</span>
</div>
<a href="https://github.com/ashutro/Query-gpt-ollama" target="_blank" rel="noopener noreferrer"
class="github-link">
<svg viewBox="0 0 24 24" width="20" height="20" stroke="currentColor" stroke-width="2"
fill="none" stroke-linecap="round" stroke-linejoin="round">
<path
d="M9 19c-5 1.5-5-2.5-7-3m14 6v-3.87a3.37 3.37 0 0 0-.94-2.61c3.14-.35 6.44-1.54 6.44-7A5.44 5.44 0 0 0 20 4.77 5.07 5.07 0 0 0 19.91 1S18.73.65 16 2.48a13.38 13.38 0 0 0-7 0C6.27.65 5.09 1 5.09 1A5.07 5.07 0 0 0 5 4.77a5.44 5.44 0 0 0-1.5 3.78c0 5.42 3.3 6.61 6.44 7A3.37 3.37 0 0 0 9 18.13V22">
</path>
</svg>
View Repository
</a>
</div>
<div class="project-card">
<h3 class="project-title">Query-gpt-codellama-7b</h3>
<p><strong>Architecture:</strong> Local LLM-powered natural language to SQL converter built on
FastAPI and SQLite, allowing users to execute dynamic queries entirely via local Code Llama
models.</p>
<div class="project-tech">
<span>FastAPI</span>
<span>Code Llama</span>
<span>SQLite</span>
<span>Python</span>
</div>
<a href="https://github.com/ashutro/Query-gpt-codellama-7b" target="_blank"
rel="noopener noreferrer" class="github-link">
<svg viewBox="0 0 24 24" width="20" height="20" stroke="currentColor" stroke-width="2"
fill="none" stroke-linecap="round" stroke-linejoin="round">
<path
d="M9 19c-5 1.5-5-2.5-7-3m14 6v-3.87a3.37 3.37 0 0 0-.94-2.61c3.14-.35 6.44-1.54 6.44-7A5.44 5.44 0 0 0 20 4.77 5.07 5.07 0 0 0 19.91 1S18.73.65 16 2.48a13.38 13.38 0 0 0-7 0C6.27.65 5.09 1 5.09 1A5.07 5.07 0 0 0 5 4.77a5.44 5.44 0 0 0-1.5 3.78c0 5.42 3.3 6.61 6.44 7A3.37 3.37 0 0 0 9 18.13V22">
</path>
</svg>
View Repository
</a>
</div>
<div class="project-card">
<h3 class="project-title">learn-apache-airflow</h3>
<p><strong>Architecture:</strong> A comprehensive Apache Airflow repository containing step-by-step
DAGs, orchestration strategies, and workflow automation for modern data engineering pipelines.
</p>
<div class="project-tech">
<span>Apache Airflow</span>
<span>DAGs</span>
<span>Workflow</span>
<span>Orchestration</span>
</div>
<a href="https://github.com/ashutro/learn-apache-airflow" target="_blank" rel="noopener noreferrer"
class="github-link">
<svg viewBox="0 0 24 24" width="20" height="20" stroke="currentColor" stroke-width="2"
fill="none" stroke-linecap="round" stroke-linejoin="round">
<path
d="M9 19c-5 1.5-5-2.5-7-3m14 6v-3.87a3.37 3.37 0 0 0-.94-2.61c3.14-.35 6.44-1.54 6.44-7A5.44 5.44 0 0 0 20 4.77 5.07 5.07 0 0 0 19.91 1S18.73.65 16 2.48a13.38 13.38 0 0 0-7 0C6.27.65 5.09 1 5.09 1A5.07 5.07 0 0 0 5 4.77a5.44 5.44 0 0 0-1.5 3.78c0 5.42 3.3 6.61 6.44 7A3.37 3.37 0 0 0 9 18.13V22">
</path>
</svg>
View Repository
</a>
</div>
</div>
</section>
<section id="certifications" class="section fade-in">
<h2 class="section-title"><span>05.</span> Certifications</h2>
<div class="cert-grid">
<div class="cert-card">
<h3>GCP Certified Associate Cloud Engineer</h3>
<p>Associate Level (2024)</p>
</div>
<div class="cert-card">
<h3>Databricks Certified Data Engineer</h3>
<p>Associate Level (2025)</p>
</div>
<div class="cert-card">
<h3>AWS Certified Data Engineer</h3>
<p>Associate Level (2025)</p>
</div>
<div class="cert-card">
<h3>AWS Certified Developer</h3>
<p>Associate Level (2025)</p>
</div>
<div class="cert-card">
<h3>AWS Certified Cloud Practitioner</h3>
<p>Foundational Level</p>
</div>
</div>
</section>
<section id="education" class="section fade-in">
<h2 class="section-title"><span>06.</span> Education</h2>
<div class="timeline education-timeline">
<div class="timeline-item">
<h3 class="role">B.Tech. in Computer Science Engineering (CSE)</h3>
<h4 class="company">Dr. Ambedkar Institute of Technology for Handicapped</h4>
<p class="date"><time datetime="2018">2018</time> – <time datetime="2022">2022</time> | Kanpur,
India</p>
</div>
</div>
</section>
<section id="contact" class="section fade-in">
<h2 class="section-title"><span>07.</span> Contact</h2>
<div class="social-links">
<a href="mailto:iamashutosh.kumar.dev@gmail.com" class="social-btn btn secondary-btn"
aria-label="Send email to Ashutosh Kumar">Email Me</a>
<a href="https://linkedin.com/in/ashutro" target="_blank" rel="noopener noreferrer"
class="social-btn btn secondary-btn" aria-label="Ashutosh Kumar LinkedIn profile">LinkedIn</a>
<a href="https://github.com/ashutro" target="_blank" rel="noopener noreferrer"
class="social-btn btn secondary-btn" aria-label="Ashutosh Kumar GitHub profile">GitHub</a>
</div>
</section>
</main>
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