Annex Risk | Backend + Data Engineer | Bay Area, Austin | Full-time
The cost of homeowners insurance, especially in natural catastrophe prone areas, has skyrocketed in recent years. Annex is solving this problem with better digital infrastructure for executing the transaction. Our platform enables insurance agents to quote and bind policies within 90 seconds vs. the current multi-day phone/email/fax-based transaction that is the status quo.
We grew 4x in 2025 and anticipate similar growth in 2026. We've done the above after deploying <$1.5m in funding. We have significant and growing momentum on both the insurance distribution and insurance capital sides of our business; our challenge now is scaling our engineering team to support that progress.
We're in that exciting early stage, where someone can contribute foundational new pieces to core parts of our platform.
Annex Risk | Backend + Data Engineer | Bay Area, Austin | Full-time
The cost of homeowners insurance, especially in natural catastrophe prone areas, has skyrocketed in recent years. Annex is solving this problem with better digital infrastructure for executing the transaction. Our platform enables insurance agents to quote and bind policies within 90 seconds vs. the current multi-day phone/email/fax-based transaction that is the status quo.
We grew 4x in 2025 and anticipate similar growth in 2026. We've done the above after deploying <$1.5m in funding. We have significant and growing momentum on both the insurance distribution and insurance capital sides of our business; our challenge now is scaling our engineering team to support that progress.
We're in that exciting early stage, where someone can contribute foundational new pieces to core parts of our platform.
Annex | Backend + Data Engineer | Bay Area, Austin | Full-time
The cost of homeowners insurance, especially in natural catastrophe prone areas, has skyrocketed in recent years. Annex is solving this problem with better digital infrastructure for executing the transaction. Our platform enables insurance agents to quote and bind policies within 90 seconds vs. the current multi-day phone/email/fax-based transaction that is the status quo.
We grew revenue 6x last year and should end 2025 having grown 3-4x. We've done the above after deploying <$1.5m in funding. We have significant and growing momentum on both the insurance distribution and insurance capital sides of our business; our challenge now is scaling our engineering team to support that progress.
We're in that exciting early stage, where someone can contribute foundational new pieces to core parts of our platform.
Annex Risk | Product Engineer | Bay Area | Full-time
The cost of homeowners insurance, especially in natural catastrophe heavy areas, has skyrocketed in recent years. Annex Risk is solving this problem with better digital infrastructure for executing the transaction. Our platform enables insurance agents to quote and bind policies within 90 seconds vs. the current multi-day phone/email/fax-based transaction that is the status quo.
We recently closed our seed round, are growing 30-100% month over month, and we're looking for a full-stack product engineer to be our first engineering hire. The ideal candidate has experience designing and shipping product features end-to-end in a fast-paced environment.
What will you get to build?
- Extensions to our core platform to integrate with additional carrier partners
- Improvements to our agent portal and API infrastructure
- A consumer portal to allow insureds to access their docs, make payments, and request policy changes
Our tech stack is primarily Scala (Finatra + Finagle!), Javascript (React + Retool), and Temporal.
come to say this. code complete is basically a free ticket to obtain 10 years of working experience in one go. I read it after 3 years of career, and still found the content insightful
I'd also add the mythical man month. being a programmer is good, but overspecializing as the technical guy cause a certain degree of blindness to other software requirements (cost, time, quality). some part of that books can't really work in today work environment because external constraint, like the surgical team, but every word of it is gold.
We've recently adopted Spark SQL and our queries run 5 - 200x times faster than with Hive.
Our experience with Spark Streaming, on the other hand, has been mixed. Our streaming app runs stably most of the time (up to 4 days in some cases), but we still see the occasional failure, sometimes with no exception or stack trace indicating what failed.
Our goal is to have a 24/7 streaming service, and Spark has gotten us close to that. There are just a couple of unexplained errors standing in our way.
1. Start at the simplest subsystem, and work from there.
This is especially true when working with third-party, legacy, or undocumented code. In the absence of documentation, your only way forward is to read the source code[0]. Find the main() of the simplest/smallest module in the system, and begin tracing through the code. This could be with a debugger, print statements, or the search functionality in your editor/IDE (Find Usages and Go to Declaration in Intellij are lifesavers).
2. Don't be afraid to break things apart.
If the simplest module in the system is overwhelmingly complex, start commenting out parts of the code. Go until you have effectively reduced it to "Hello, World", if you have to. From there, you can gradually add features back.
3. Constantly test your assumptions.
Don't assume comments do what they say they do.
Don't assume that config flag produces the behavior the documentation claims it does.
Do take inventory of you assumptions whenever the behavior of the system contradicts your current understanding of how it works.
You should be able to back up any claims about the system with empirical evidence e.g. when I change A to B, X happens; if I change A to C, Y happens.
Intellij does a very good job for most of my programming and I use it daily.
In the process of learning a distributed system, the process of looking at the code as its documentation is not the ideal way. One could spend enormous amount of time to just understand a simple business usecase.
But then just because it takes time doesn't make it untrue.
I would rather accept a system which self documents itself with every change.
The point of breaking things is all well and good but to truly understand everything, after breaking also one needs to put the together one by one and understand their interactions so that if any one in the pool of modules fails, one knows what exactly would have caused it and how to be more resilient towards failures.
It's completely different from person to person, but I use a whiteboard to draw up different components and their interactions. Then work my way down subsystems when I need to. Having that overview is very beneficial.
TellApart - Burlingame, CA. New York City (Full-Time, Intern, Visa): Software Engineering
TellApart helps many of the world’s most successful retailers unlock the power of their customer data by applying the latest advances in cloud computing, predictive analytics, and machine learning.
Our Customer Data Platform collects and analyzes massive amounts of data to power an integrated suite of marketing solutions that delivers personalized shopping experiences for 100s of millions of consumers in real time.
- Scale our existing data pipeline to handle 10x the data to match our current growth trajectory. (We are currently one of AWS’ biggest customers and only have a 50 person engineering team)!
- Research, design, and launch dual-homed, distributed storage systems that allow single digit millisecond access times to satisfy the stringent 40ms request time real-time bidding constraints.
- Migrate our legacy identity system used for audience targeting and real time bidding, to a new identity system built with concepts taken from the Lambda architecture
- Make performance optimizations to our real time bidding service to bring 99.9th percentile latency to within 30ms
The cost of homeowners insurance, especially in natural catastrophe prone areas, has skyrocketed in recent years. Annex is solving this problem with better digital infrastructure for executing the transaction. Our platform enables insurance agents to quote and bind policies within 90 seconds vs. the current multi-day phone/email/fax-based transaction that is the status quo.
We grew 4x in 2025 and anticipate similar growth in 2026. We've done the above after deploying <$1.5m in funding. We have significant and growing momentum on both the insurance distribution and insurance capital sides of our business; our challenge now is scaling our engineering team to support that progress.
We're in that exciting early stage, where someone can contribute foundational new pieces to core parts of our platform.
More details at:
Data Engineer: https://wellfound.com/jobs/3509845-software-engineer-data-en...
Backend Engineer: https://wellfound.com/jobs/3060211-software-engineer-backend