My journey into gaming analytics started with a beaten-up SEGA 16-bit console in the late '90s, blasting through Contra and Teenage Mutant Ninja Turtles with the kind of obsessive dedication I'd later apply to debugging production pipelines at 3 AM. Like most kids, I had no idea those pixelated adventures were generating exactly zero data points—because nobody was tracking my completion rates, optimal routes, or how many times I rage-quit before finally beating that boss.
Fast forward through countless hours of Need for Speed at my cousin's place (where I held the undefeated lap record, thank you very much), a 2007 PC that became my platform for mastering Hitman's stealth mechanics and dominating NFS Most Wanted, and eventually college. I earned my Bachelor's in Electronics and Communications Engineering (2009–2013), then did what my parents thought was the responsible thing: I got a stable job in insurance. For two years, I stared at black and green terminal screens, writing COBOL and navigating 20,000-line procedural monoliths that were probably written when Contra was still in arcades. If you've never experienced the joy of debugging mainframe code where a misplaced period can break everything and EVERYTHING-IS-SCREAMING-IN-ALL-CAPS, consider yourself blessed.
Here's the thing about insurance software work: it's stable, it's important, and it taught me valuable skills—like patience, resilience, and a deep appreciation for modern development practices. But adding fields to insurance forms in COBOL will never give you the same rush as understanding why millions of players willingly drive off cliffs in GTA just to see what happens, or why someone drops $20 on a virtual weapon skin they'll use for exactly one match. After two years of thinking "this would be so much more interesting if we analyzed player behavior instead of debugging Policy Table Column 47" while my eyes adjusted to green phosphor glow, I realized I was optimizing for the wrong problems. My NFS lap records and that kid who beat Contra were calling me back.
The Pivot
So I made a bet on myself. In 2015, I moved to the United States to pursue a Master's in Management Information Systems at the University of Illinois, specializing in Data Mining and Machine Learning. Here's what clicked during my first semester: All those years of gaming—memorizing enemy patterns in Contra, optimizing lap times in Need for Speed, planning stealth routes through Hitman, figuring out why certain strategies worked better than others—I was basically doing data analysis without realizing it. Observing systems, identifying patterns, testing hypotheses, optimizing outcomes. Gaming had trained me to think analytically about behavior and systems before I ever wrote a line of SQL. The technical foundation from my engineering degree plus this analytical mindset? That was the combination I didn't know I needed.
The Break
In 2017, fresh out of grad school, I landed at a consulting firm called Affine Analytics for what seemed like a standard data analyst role. Then came the career-defining moment: my client assignment was drum roll Activision Blizzard, working on Call of Duty. That kid who'd beaten Contra without the Konami code, who'd left insurance because the data wasn't exciting enough? Now writing SQL queries to understand how millions of players experienced one of the world's biggest gaming franchises.
The Launch That Changed Everything
But the real plot twist came in 2019. I became one of the founding analysts on a new mobile title that Activision was betting big on: Call of Duty Mobile. What happened next exceeded everyone's wildest projections. The game shattered mobile gaming records with 100 million downloads in its first week—the biggest mobile game launch in history, crushing competitors like Fortnite (22 million) and PUBG Mobile (28 million). By the end of year one, we'd crossed 270 million downloads. Within eight months, we hit 250 million downloads—more than Fortnite accumulated in the same timeframe (78 million).
I was there from day one, analyzing player behavior across what would eventually become 100 million monthly active users at peak. The game won Mobile Game of the Year in 2019, was nominated again in 2020, and has since surpassed 1 billion lifetime downloads and generated over $3 billion in revenue.
My analyses directly informed:
- Retention strategies
- Monetization optimization (including one A/B test that reduced production costs by 75%)
- Critical infrastructure decisions—like identifying Quality of Service issues that, once resolved through adding 5 new data centers, recovered 60% of Android retention drops and 100% of iOS issues in affected regions
Today
Today, I'm a Data Engineer at Amazon Games, where I've scaled from analyzing 100 million mobile users to architecting infrastructure that processes over 5 billion player events every single day across multiple game portfolios. The journey from player to engineer to insurance developer to gaming analytics specialist to infrastructure architect taught me that the best career moves aren't always linear—sometimes you need to take a detour to find your path. And sometimes, betting on your passion pays off in ways you never imagined.
This is the first in a 10-part series where I'll pull back the curtain on gaming analytics at scale—from breaking into the industry to architecting systems that serve millions of concurrent players. Whether you're a data professional curious about gaming, someone contemplating a career pivot (like I did from insurance), or an experienced engineer scaling your infrastructure, I'll share the technical architecture, business strategy, and hard-won lessons from building analytics systems for some of the world's biggest games.
Breaking Into Gaming Analytics: Where to Start
After nine years in this industry—from analyst to data engineer, mobile games to AAA titles—here's what I wish someone had told me when I was staring at those green COBOL screens wondering how to make the jump.
The Reality Check
Gaming analytics isn't a single role—it's a spectrum. You could be:
- Analytics Consultant/Analyst (SQL-heavy, business insights, A/B testing)
- Data Scientist (ML models, churn prediction, player segmentation)
- Data Engineer (pipelines, infrastructure, real-time processing, data modeling)
- Analytics Engineer (dbt, data modeling, metrics definitions)
I started as an analyst and evolved into engineering. Your path will be different, and that's fine.
Skills That Actually Matter
The Non-Negotiables:
- SQL - You'll live in it. Window functions, CTEs, query optimization.
- Python - Pandas, data manipulation, basic scripting at minimum.
- Business Acumen - Understanding DAU, retention curves, ARPU, LTV.
The Differentiators:
- Cloud platforms - AWS (my world) or GCP. Certifications help.
- Orchestration - Airflow experience is gold in 2025.
- Real-time processing - Kafka, Kinesis, streaming architectures.
- Gaming domain knowledge - Play games. Understand F2P vs premium economics.
Don't Sleep On:
- Data visualization (Tableau, Looker)
- A/B testing and Quasi Experimental frameworks, and statistical rigor
- Cross-functional communication (you'll work with product, marketing, live ops)
Practical Steps (The Playbook)
If you're currently outside gaming:
Build a portfolio project - Analyze public gaming datasets (Steam, Riot API, mobile game data). Show you understand retention curves, cohort analysis, player segmentation. Showcase your projects by building a portfolio website that you can easily share through email or LinkedIn messages.
Target the right companies - Mobile gaming studios (King, Scopely, Rovio) are more analytics-mature than many indie/AA console studios. Start where data teams are established.
Network strategically - GDC (Game Developers Conference) has an analytics track. Join gaming analytics Slack/Discord communities. LinkedIn works.
Learn the language - Read Newzoo reports, Deconstructor of Fun blog, GameRefinery analysis. When you interview, speak the business language.
Consider the side door - Consulting firms (like Affine, where I started) often have gaming clients. It's how I got in.
The Uncomfortable Truth
Breaking into gaming analytics from outside isn't easy. The industry values domain experience, and the "we only hire from gaming" mindset is real at some studios.
But it's not impossible:
- I came from insurance COBOL
- My colleague came from healthcare analytics
- Another came from fintech engineering
What got us in: Genuine passion + proven technical skills + understanding the business. And sometimes, a bit of luck with timing.
What's Next in This Series
Over the next 9 posts, I'll go deep on everything I've learned:
- Part 2: The Gaming Analytics Tech Stack: From Ingestion to Insights
- Part 3: Building Event Pipelines at Scale
- Part 4: Near Real-Time Analytics (why not "true" real-time)
- Part 5: Data Modeling for Games (Star Schema)
- Part 6: Player Retention & Engagement Analytics
- Part 7: Game Economy & Monetization
- Part 8: Infrastructure as Code with AWS CDK
- Part 9: Data Quality & Governance at Scale
- Part 10: Leading Analytics Teams & Scaling Impact
Each post will include real architectures, code examples, and lessons from systems serving millions of players.
About Me: I'm Sai Krishna Chaitanya Chigili, Data Engineer at Amazon Games, where I architect infrastructure processing 5B+ daily player events. Previously founding analyst for Call of Duty Mobile at Activision Blizzard.
Connect: LinkedIn | GitHub | chigili.dev
Questions about breaking into gaming analytics? Drop a comment or DM me on LinkedIn. I try to respond to everyone.
Don't miss the series: Follow me here on DEV for Parts 2–10.
Originally published on Medium
If this post helped you, give it a ❤️ and share it with someone considering a career in gaming analytics. And if you're already in the industry, I'd love to hear your journey in the comments.
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