Chandra Sekhar Nayak
Frontend leadership at Moneyview
Bengaluru, India
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I lead client engineering at Moneyview -- Android, iOS and server-driven UI, plus the app-facing BFF and platform teams behind it. Six teams, three Engineering Managers, and one of India's largest credit-led lending platforms underneath.
I joined in 2021 as the second engineer on mobile, and monthly disbursals have grown roughly 30x since. Most of what I've done is build things that didn't exist yet:
→ An agentic voice loan journey that takes a customer from application to disbursal entirely in conversation, in 10+ Indian languages. Live in pilot, with the strongest completion rates in tier-3 towns -- a segment the app-only funnel never reached.
→ Moneyview's first iOS app, which I built end-to-end as the sole engineer, then made the business case to fund a dedicated team. It's now around 5% of monthly disbursals.
→ WhatsApp lending, which started in an internal hackathon and now drives a similar share.
The less visible work matters as much. We replaced three third-party SaaS products with in-house telemetry, session recording and remote configuration -- a large annual vendor bill gone, and sensitive customer data stopped leaving our own infrastructure. We moved the app off blanket permission prompts at launch to product-scoped consent, so someone who only uses UPI never hands over SMS access, and I architected the service that now holds consent and personal data centrally for every product. Registration completion went up 10–12%. Collecting less data made us more money, which is not the trade-off anyone expects going in -- and that work is also what regulatory readiness looks like from the engineering side.
On teams: I've grown this org from a handful of engineers into a multi-team organisation, promoted people into Engineering Manager and Staff Engineer roles from inside, and held attrition at roughly 4% a year. About 30% of the engineers I've hired came through BlrKotlin, the Kotlin community I've run in Bengaluru since 2017 -- now 5,000+ members.
The app itself: 100M+ downloads, 4.8★, and 99.95–99.97% crash-free users, with several releases at 100%.
I also wrote "Hands-On Data Structures and Algorithms with Kotlin" for Packt, and I've spoken at DroidKaigi in Tokyo, Google DevFests, and JUGs around Bengaluru.
Always happy to talk engineering leadership, scaling teams, and Kotlin — nayak.chandra1@gmail.com
Area of Expertise
Topics
Sneaking inside Kotlin features
Kotlin has many language features even though none of them are supported by JVM or Android ART. This talk will go through all the language features and understand how they work internally for JVM or ART compatibility.
Many developers in our community uses all these APIs without knowing how they work. After this talk, they will have a good understanding of how those features work.
Bringing MATLAB like syntax to Kotlin
MATLAB is a very powerful language when it comes to Matrix. We can definitely do the same in Kotlin but with cryptic syntax.
This talk goes through the syntax of MATLAB one by one and does the same in Kotlin which looks similar to the MATLAB syntax. We will see lot of examples and understand how various Kotlin features can be leveraged to build a good syntactical API to it a fun language for Matrix manipulation.
Now a days ML is trending and Matrix is building block for those. By the end of this talk, audience will feel that Kotlin can also be good alternative in ML world.
Sneaking inside JVM language features
JVM languages like Kotlin, Scala has many language features even though none of them are supported by JVM. This talk will go through all those language features and understand how they work internally for JVM compatibility.
For example:
1. Do all know, how features like default arguments and named arguments works?
2. Do all know, that switch statement can only work with integers? Then how when expression works with almost all data types?
3. Do all know, how inline classes works?
4. And many more features.
Many developers uses all these APIs without knowing how the respective compilers generate code underneath to make it JVM compatible. After this talk, they will have a good understanding of how these features work internally which obviously make them a better programmer.
Sneaking inside Kotlin features (Part II)
Kotlin has many language features even though none of them are supported by JVM or Android ART. This talk will go through all the language features and understand how they work internally for JVM or ART compatibility.
For example:
1. Do all know, how features like default arguments and default methods (in interface) works?
2. Do all know, that switch statement can only work with integers? Then how when expression works with almost all data types?
3. Do all know, how inline classes works?
4. And many more features.
Many developers in kotlin community uses all these APIs without knowing how they work. After this talk, they will have a good understanding of how these features work internally which obviously make them a better programmer.
As the same topic is already presented at DevFest Kolkata 2019 and going to be presented at DroidKaigi 2020, I will make this talk with few different examples and features.
None to Manual to Dagger (A DI Journey)
Everyone starts learning dagger from its annotations (e.g. from @Component to @Scope), and finds it difficult to learn. However, we can learn it using an opposite approach and learn it better. This talk is going to discuss about that reverse approach to learn dagger.
Instead of learning what is @Component and how to use it, this talk first goes through something
which doesn't have any DI and then discuss about the ways to inject the dependencies manually. In this
journey of migrating from "No DI" to "Manual DI", we will end up at a place where our codebase looks
somewhat similar to dagger's generated code. Now is the time to replace those with proper dagger annotations and make it a journey from "Manual DI" to "Dagger".
In this journey, we will see a lot of Dagger's generated code first (as if we wrote them ourselves) and
then Dagger's annotations. This will make everyone visualise which Dagger annotation is doing what job and which should be used when.
Target Audience - Beginner
What's inside an Rx Chain
RxJava lets us think data as a stream. Each Rx Chain does three major work (Creates the stream,
Manipulates it through multiple operators and Terminates the stream ones consumed). One of the most powerful thing about RxJava is its operators. We use them heavily without even knowing what these operators actually do under the hood. In this video, we will explore all these operators and understand them better.
For Example, we will go through the internal workings of simple operators like filter { ... } and map { ... }
to more complex operators like flatMap { ... } etc.
We will see how Observables are created internally and how the created Observable are passed to one operator and gets assembled with a new Observable and passed into another operator downstream.
We will also see how operator fusion plays a very good role in the Rx chain.
ML == REST
Machine Learning is not everybody's cup of tea. It requires a lot of skill on Algebra and related algorithms. Does that mean, we should ignore this skill and move ahead? Of course not!!! Now a days, there are many solutions who exposes the ML capabilities as Restful Web Services. In this talk, we are going to explore one such tool called "Firebase ML Kit".
Firebase ML Kit is a tool, by integrating which, our app enables to solve lot of ML problems with ease. And the best part is, we neither need to learn Python nor Algebric algorithms. It will feel as if we are consuming some Restful API through a wrapper called "Firebase ML Kit".
There are lot of features provided by Ml Kit, we will go through many of them and try to understand how easy it is to enable an Android app to have ML capabilities. As the talk proceeds, audience will have a confidence of trying to enhance their existing app with some of the features discussed.
The motive of this talk is to let the audience know that adding ML features in their app is like consuming some Restful Web Service. And because of this many developers who have already used "Firebase ML Kit"
or might want to try it in near future, may have a misconception that ML == REST. However, that is not true. Machine Learning can never be equal to any Restful Web Service. By the end of the talk, We will conclude with ML != REST.
DroidKaigi 2020 Sessionize Event
DevFest Kolkata 2019 Sessionize Event
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