Building startups

You are not short on effort.

You are short on signal.

The usual cycle is dashboards, debates, and interviews that start at zero.

Roadmaps wobble. Messaging stalls. Confidence dips.

There is a calmer way. Start with what your market already says in public.

Turn open-web signals into patterns you can brief the team with.

Pair that with a simple monthly view of how competitors and language shift so decisions stop feeling like a guess.

That’s where I come in.

I run TrendLoom’s Market Radar, a high-touch insight system that gives Seed–Series A teams a continuous, structured read on customers, category, and competitors.

It uses only ethical, public data and an academically grounded framework that combines statistics, ML, and LLMs.

Interviews are not replaced. They are sharpened so you walk in with better hypotheses and leave with decisions you can defend.

I have advised 100+ founders on GTM, messaging, and discovery, and built Zaqlick from my Kellogg-WHU EMBA research into AI-assisted personas. I treat AI as an engine inside a method, not a magic trick.

I help with

• Open-web signals turned into clear patterns
• Competitive intelligence that tracks what changed vs last month
• Product discovery that starts deeper, not from zero
• Messaging that uses real customer language
• Decision protection for roadmap and GTM

Results

• Teams align faster because debates shift from opinions to signals
• Interviews get sharper, messaging tightens, and weak bets are dropped
• Leaders report a steadier sense of “what’s happening now” month to month

There are a few ways to work together:

Start with a 15-minute Signal Check. Bring one question. I will tell you if strong signals exist, what they suggest, and the next best move. If you want deeper support, we roll into Market Radar.

DM me the word “SIGNAL” to see if you are a fit and which path makes sense.

Elena Tsemirava

⚙️ I help engineering teams solve complex performance, scalability, and security challenges — from backend architecture to build pipelines.

I specialize in optimizing systems and infrastructure to handle scale, improve reliability, and accelerate delivery. Whether you're dealing with application slowdowns, CI/CD inefficiencies, or compliance bottlenecks, I bring practical, hands-on expertise to fix them.

My work spans:

    🔧 Performance tuning (applications, databases, infrastructure)

    🚀 Build process optimization (CI/CD pipelines, automation, release flow)

    ☁️ Cloud infrastructure (AWS-focused, IaC, monitoring, auto-scaling)

    🔒 Security & compliance (ISO 27001, SOC 2 readiness, secure design)

I work closely with teams to identify root causes, design scalable solutions, and implement improvements that last — both technically and operationally.

📈 If your systems are slowing you down, your compliance goals feel overwhelming, or your delivery pipeline needs streamlining, I can help you move faster without breaking things.

🔗 Let’s connect if you need help scaling securely and efficiently — or if you just want to swap notes on building better systems.

Viktar Patotski

6 years working at the top level with founders.
CTO, Lead Architect, Head of Backend Engineering — across startups and scale-ups.
Here are the 3 most important things it taught me about engineering priorities.

1. Speed is not the enemy of quality. Context is everything.
Early in my career I pushed back hard on "just ship it" culture.
I was wrong.
A founder racing to close their first enterprise client doesn't need a perfect system. They need a working one — today.
Understanding the business stage completely changed how I make technical decisions. The right architecture at pre-seed looks nothing like the right architecture at Series B.

2. Business context changes every technical decision.
I used to evaluate technical choices in isolation.
Best performance. Best scalability. Best tooling.
Then I started asking different questions.
What's the runway? Who's the next customer? What does the board expect in 90 days?
Suddenly the "best" technical decision looked completely different. Engineering doesn't exist in a vacuum — it serves a business. The faster you internalise that, the better engineer you become.

3. Founders don't care about your technology. They care about outcomes.
No founder has ever asked me: "What database engine are you using?"
They ask: "Will it scale when we hit 100K users?" "Can we ship the new feature before the conference?" "How much will this cost us in 6 months?"
Learning to speak that language — translating technical decisions into business outcomes — is the single most valuable skill I've developed in 6 years.
It's also what separates engineers who get a seat at the table from those who don't.

6 years. Dozens of founders. One consistent truth:
The best technical decisions are business decisions first.

♻️ Repost if this resonates with someone on your team.
💬 What's the biggest shift in your engineering thinking when working close to business? I'd love to hear it.

#SoftwareEngineering #TechLeadership #StartupEngineering #EngineeringCulture #Founders

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One thing I didn’t include in the original post:
In 60-minute reviews, data flow clarity often exposes more issues than people expect.
If the team can’t easily explain how data moves through the system — that’s a red flag.

Oracle Certified Professional: Java SE 11 Developer

smart. safe. samples
It is all about the quality of medical decisions.
 
 S4DX closes the data gap between blood collection and lab analysis and enables a new quality standard for medical diagnostic decision making. We change preanalytics from a trust-based system into an evidence-based system. Our digital human sample fingerprint is a quantum leap forward for patient safety, the quality of medical decisions and lab testing efficiency. We create a trustworthy and verifiably basis for the personalized medicine of the future.

Duties:
 🧪 responsible for the design and architecture of the S4DX cloud solution
 🧪 leading cloud team
 🧪 requirements gathering from internal and external stack holders
 🧪 designing infrastructure solution
 🧪 design of deployment and release strategy
 🧪 design of monitoring solution
 🧪 all of the above while still staying hands-on and actively contributing to the code base 

Achievements:
 🧪 improved scalability of the system
 🧪improved performance of solution by 3x
 🧪 optimizing cloud running cost by 3x
 🧪 improving and speeding up deployment procedures
🧪 successfully passing audits for ISO 27001, ISO 9001 certifications
🧪 successfully went through a penetration test

Tools:
🛠️Java
🛠️Spring Boot and all-around Spring
🛠️PostgreSQL
🛠️Kafka
🛠️AWS
🛠️Terraform
🛠️Jira

Lead Cloud Developer/ Architect

My duties here @Fernride:
 - Architecture design and implementation of Fernride's backend
 - Managing cloud infrastructure and solution deployment
 - Maintenance and monitoring
 - Leading backend team

Tech stack:
🪛 JVM world: Java, Spring Boot, JPA and all around
🪛Azure cloud
🪛Postgresql, Hyperscale
🪛Terraform
🪛Docker
🪛Linux

Lead Backend Developer 

Building a streaming analytics platform for processing IoT generated data using various Machine Learning models, which includes REST APIs built using Spring Boot and processing pipeline built with Apache Spark.

Day to day responsibilities
 • design and build the streaming analytics platform
 • continuous improvement of development practices and infrastructure
 
Main tools/frameworks used:
 ⚒ Spring Boot
 ⚒ Apache Spark
 ⚒ Apache Kafka
 ⚒ Docker
 ⚒ AWS
 ⚒ Kubernetes
 ⚒ PostgreSQL

Senior Software Backend Developer

Day to day responsibilities: leading a team of 5 developers, which includes: discussion, design and implementation of new features as well as providing code review. Other than that also making sure current process is followed and constantly making improvements to process. Also handling cross team coordination. Technologies used: Spring, (Spring MVC, Spring security, Spring Boot and other Spring libs) Hibernate, PostgreSQL, Redis, RabbitMQ, AWS, Linux, JUnit (Spring-test, Mockito DBunit and other testing libs), Gradle, Ansible, NewRelic

Tech/Team Development Lead

Development Team Lead

Working on own projects, contributing to open source projects

Software Developer

Software Development Team Lead - leading a team of 10-17 members. Working on big Case management system with built in Process, Document management functionality, called Case360 for OpenText (ex. Global360).

Software Development Team Lead

Playing a key developer role in a team, working on big Case management system with built in Process, Document management functionality, called Case360 for OpenText (ex. Global360).

Senior Software Engineer

Java Software Engineer - developing different software applications in a team using Java programming language and various Java based frameworks.

Software Engineer

Backend & Infrastructure Architect |Cloud | Scalability & Performance Expert | ISO 27001 & SOC 2 Compliance | Secure Systems at Scale

Helping startups survive their own success | Cloud & Software Architect | Cutting cloud costs · Eliminating bottlenecks · Building for scale · Securing what you build

One schema decision. 55% faster queries. 19% less storage/RAM. Zero write penalty.

Most developers default to VARCHAR for status columns.

It's the path of least resistance. It works. Nobody questions it.

So I ran a benchmark on PostgreSQL 17 — 1 million rows, AWS RDS, 50 runs each — to find out exactly what that default is costing you.

The setup
Two identical tables. Same 1M rows. Same 10 status values. One uses VARCHAR. One uses ENUM.

Infrastructure: EC2 t3a.medium client → RDS db.t4g.medium (PostgreSQL 17.6), same VPC.

📦 Storage & RAM

Table: VARCHAR 99.5 MB → ENUM 80.5 MB (19% smaller)
Index: identical (28.2 MB vs 28.1 MB)
Total: VARCHAR 127.8 MB → ENUM 108.7 MB
Smaller data = more fits in buffer cache = faster queries. In an era where RAM prices are skyrocketing, that's a free instance upgrade.

✍️ Write Performance

INSERT performance: identical (~18–19s per 10K rows, with or without index).
No downside on writes. Zero.

⚠️ The one real trade-off

Adding a new ENUM value requires an ALTER TYPE — a DDL change.
VARCHAR accepts any string without a migration.
If your status values change frequently → VARCHAR may be the right call. If they're stable (active, inactive, pending, banned) → ENUM wins every time.

Know your data. Choose your types deliberately.
This is the kind of decision that compounds quietly over millions of rows — until one day your DBA asks why your queries are slow and your RAM is always full.

💬 Are you using ENUM in your schema or defaulting to VARCHAR? What's your reasoning?

#PostgreSQL #DatabasePerformance #SoftwareEngineering #BackendDevelopment #StartupEngineering #CloudArchitecture

VARCHAR vs ENUM — PostgreSQL 17 Benchmark

A founder friend came to me last year frustrated.
Their app was slow. Users were complaining. The team had already upgraded their server infrastructure twice.
It didn't help.
I spent 2 hours looking at their codebase. The server infrastructure was fine. The problem was hiding in plain sight — in 3 places nobody had thought to check.

❌ Problem 1: N+1 queries quietly destroying their DB
Their product listing page was making 1 query to fetch products — then 1 separate query per product to fetch its details.
50 products on the page = 51 database queries. Every. Single. Request.
One query fixed it. Page load dropped by 60%.

❌ Problem 2: Synchronous calls blocking everything
Their order confirmation flow was processing payment → sending email → sending analytics → updating accounting software to record the invoice — all in sequence, one after another.
The user was sitting on a loading screen waiting for an email to send before seeing a confirmation.
We identified everything that wasn't critical to completing the checkout and moved it async — email, analytics, accounting. Checkout response time went from 6s to 500ms.

❌ Problem 3: Payment provider API call sitting in the critical path
Every time a user loaded their billing page, the app made a live call to their payment provider to fetch subscription and charges details.
Payment providers average 300–600ms per call. On a bad day — much more.
We cached the response. Billing page went from 700ms to 80ms.

Total time to fix all 3: 2 days.
The founder had spent weeks assuming it was an infrastructure problem. It wasn't. It never is.
Before you upgrade your server infrastructure — check your queries, your async flows, and your third-party calls in the critical path.
That's where the time is hiding.

🔍 These are the problems I love digging into.
Have you experienced something similar? Drop it in the comments — I'd love to hear your story.

#SoftwarePerformance #StartupEngineering #BackendDevelopment #CloudArchitecture #TechLeadership

Most companies approach cyber security the wrong way.

They buy expensive tools. Build walls. Install firewalls. Get a certificate.

Then an employee clicks a phishing link and it's game over.

The fortress was impenetrable. The people inside weren't prepared.

Real security isn't a product you buy. It's a culture you build.

And it lives in 4 processes most companies skip:

🧠 Security awareness & culture

Your weakest security layer is always a human being.

Not because people are careless — but because nobody trained them. Regular, practical security training isn't a checkbox. It's your first line of defence.

🔐 Access control & offboarding

Someone leaves the company. Does their access get revoked in the next 10 minutes — or the next 10 weeks?

I've seen systems where ex-employees still had full access 6 months after leaving.

No firewall protects you from that.

👁️ Audit logs & monitoring

You can't respond to what you can't see.

Logging who accessed what, when, and from where isn't glamorous. But it's what tells you a breach happened — and how far it got.

🚨 Incident response plan

When a breach happens — and it will — does your team know exactly what to do in the first 30 minutes?

Most don't. They improvise under pressure. That's when mistakes happen.

A documented, rehearsed incident response plan is worth more than most security tools combined.

The companies that get breached aren't always the ones with the weakest technology.

They're the ones where security was treated as an IT problem instead of a company-wide habit.

💬 What's the biggest security process gap you've seen in the wild?

#CyberSecurity #ISO27001 #StartupEngineering #TechLeadership #SecurityCulture #CloudArchitecture

Your dev and QA environments are running right now.
It's 2am. Nobody is using them.
AWS is charging you for every single hour.
Shutting them down outside working hours can cut your non-production bill by up to 65%.
But teams don't do it — because scheduled shutdowns cause friction.
Engineer works late. Environment is down. Takes 20 minutes to spin up. They complain. Schedule gets disabled. Back to 24/7.
Sound familiar?
The fix: on-demand control via chat
/env start staging → up in 2 minutes.
/env stop staging → down, billing stops.
Shuts down automatically at night and weekends. Anyone spins it up in seconds when needed.
Zero friction. Maximum savings.
Bonus: when environments spin up and down regularly, your team is forced to automate and improve the process. Boot time drops. Dependencies get documented. A team that spins up in 2 minutes also recovers from incidents twice as fast.
The cloud charges you for what you run — not what you use.
💬 Have you automated your environment scheduling? What worked for your team?
#AWS #CloudCosts #CostOptimisation #DevOps #CloudArchitecture

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Elena Tsemirava

Viktar Patotski

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The Battle

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Viktar Patotski

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Viktar Patotski's LinkedIn Performance

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