Hi 👋 I'm Vijay—A Problem Solver Who Hates Repetitive Work

I don't just write code. I build tools that eliminate the need to write code.

That might sound contradictory, but it's been my philosophy since day one: if you're doing something manually more than twice, you're doing it wrong. Automation isn't just a skill for me—it's an obsession.

What Makes Me Different ⚡

I'm not your typical data engineer. While most focus on building pipelines, I build platforms that empower business teams. Currently at Marks & Spencer, I'm working on loyalty programme analytics using Azure Databricks and building interactive Streamlit applications that turn complex data into actionable insights.

My Track Record Speaks for Itself:

From Bash Scripts to Building Innovation Engines

My journey started in the most unlikely place: as a Manual QA intern at Symantec, testing Norton AntiVirus for Mac. While everyone else was manually clicking through test cases, I was already asking the annoying question: *"Why can't we automate this?"

So I did. I taught myself Python and Bash scripting during my internship and started automating security test cases—port scanning, firewall validation, malicious URL detection, telemetry ping verification. What took the team days, I reduced to hours.

That's when I discovered my superpower: insatiable curiosity paired with a refusal to accept inefficiency.

My journey across companies:

• Symantec (2014-2015): Started automation obsession
• CEB → Gartner (2015-2019): Built AI/ML products after Corporate Executive Board acquisition by Gartner
• Optum (2019-2021): Full-stack + data engineering
• Eli Lilly (2021-2022): Cloud modernization
• Channel4 (2022): MarTech analytics
• Sky UK (2022-2025): Code generation innovation
• Marks & Spencer (2025-Present): Customer loyalty analytics

My Career in Numbers 📈

Technical Arsenal 🛠️

I'm a polyglot engineer who picks up technologies at an accelerated pace. Here's what I wield:

Cloud Platforms: Azure (current) • AWS • GCP (expert-level across all three)
Data Engineering: Azure Databricks • Apache Kafka • Spark • Airflow • BigQuery • Redshift • Snowflake
Languages: Python (expert) • Scala • Bash • Java • SQL • C#
Application Development: Streamlit • Flask • Django • React
Infrastructure: Terraform • Docker • Kubernetes • Infrastructure-as-Code
AI/ML: TensorFlow • NLP • LLMs • Scikit-learn
DevOps: CI/CD (Jenkins, GitHub Actions) • Monitoring (New Relic, Splunk)

But more than the tech stack, I'm driven by R&D. I'm the engineer who:

• Reads documentation for fun
• Experiments with new frameworks on weekends
• Prototypes solutions before meetings
• Builds POCs to prove feasibility

Recognition & Achievements 🏆

What My Managers Say About Me

"Vijay is, simply put, one of the most proactive and innovative engineers I have worked with. His defining trait is his deep commitment to efficiency and automation... This single innovation dramatically increased our velocity, enabling us to keep up with increasing demand and execute large-scale refactoring in record time."
— Gregory Sigaud, Data Engineering Manager, Sky UK (2022-2025)

My Career Journey:

Latest: Joined Marks & Spencer (May 2025) as Senior Data Engineer, working on loyalty programme analytics with Azure Databricks and Streamlit applications.

See the detailed breakdown with all the technical wins at my resume page.

My Philosophy: Automate Everything ⚙️

Every repetitive task represents an opportunity for innovation.

When I see manual work, I don't just see inefficiency—I see a tool waiting to be built. I see a process begging to be automated. I see velocity being left on the table.

This mindset has driven every project I've tackled:

• Automating pipeline generation to scale with demand
• Building frameworks that let teams move faster
• Creating tools that turn complex workflows into simple commands
• Eliminating toil so engineers can focus on high-value work

Why This Blog Exists 📝

Innovation without documentation is just expensive trial-and-error

This blog is my lab notebook where I document my journey from automation frameworks at Sky UK to customer analytics at Marks & Spencer. It's where I:

• Document tools I've built and the problems they solved
• Share lessons from working with Azure Databricks, Kafka, Terraform, and cloud architectures
• Explain complex data engineering patterns (streaming pipelines, medallion architecture, self-service platforms)
• Reflect on what worked, what failed spectacularly, and the lessons learned
• Share insights on building customer analytics and loyalty programme platforms

My promise: I only write about things I've actually built, debugged, deployed, and probably broken at least once in production. No theoretical fluff. Real problems, real solutions, real code.

If you're reading this and thinking "Finally, someone who gets it"—welcome. Drop a comment, share your own war stories, or just say hi.

It's way more interesting when this is a conversation, not a monologue 🍻

Tech Stack for This Blog 🛠️

This blog is hosted on Vercel, built with Next.js and Tailwind CSS using Tailwind Nextjs Starter Blog.

Why? Because I wanted a fast, modern, low-maintenance platform where I could focus on writing, not infrastructure. Sound familiar? 😉

P.S. Currently building customer loyalty analytics at Marks & Spencer using Azure Databricks and Streamlit. If you're working on similar challenges or want to discuss data platform engineering, let's connect!