About

A Welcome Message from the Founder

Hello. I am Dmitry Zinoviev, and I engineer data architecture without the marketing fluff.

Welcome to Tech Macro, an independent engineering practice. For the last decade, I have built data foundations and complex pipelines on Google Cloud for high-load platforms. I usually step in when a scaling business realizes their “modern data stack” is just a very expensive black hole. I call this “cloud chaos,” and fixing it is my primary specialty.

Why I Look For Partners, Not Clients At Tech Macro, we do not take on standard clients. A typical contractor will blindly execute your Jira tickets, even if the underlying architectural concept is objectively flawed. A partner, however, will analyze your infrastructure, politely inform you if it is a disaster, and then take full engineering responsibility to actually resolve it.

We do not just “set up the cloud” to burn your operational budget. We leverage deep engineering expertise to build resilient, custom solutions that solve actual business bottlenecks. Honest, precise, and built to scale.

  • Zero Marketing BS: If a mathematically sound F# application and a streamlined data flow can do the job, I will not sell you a massive, unnecessary multi-node cluster. Pragmatic, strongly-typed infrastructure does not break.
  • Ruthless FinOps: A well-designed data pipeline must save you money, not drain your margins. I will show you exactly how fixing one poorly written cross-join in Google BigQuery can instantly save enough cash to hire another developer.
  • Total Ownership: I build exclusively using Infrastructure as Code (IaC). You receive clean, compiled code, zero black boxes, and absolutely no vendor lock-in. You own your data entirely.

How We Begin: The Engineering Blueprint Tech Macro is a specialized practice where every engagement strictly begins with an independent, paid architectural audit and ends with a customized, zero-downtime execution. Your scaling business is too critical to run on blind guesses. Stop subsidizing cloud providers for bad code.

How We Actually Build (Engineering for Business Growth)

Let’s be honest. As a business owner or agency founder, you probably do not care about the specific syntax of our code. You care about the result: analytics dashboards that load instantly, Google Cloud bills that do not cause panic attacks, and data pipelines that do not crash at 3 AM.

The reason most “modern data setups” fail is that they are built on temporary fixes, manual configurations, and fragile scripts. It is a house of cards. We take a fundamentally different approach. We do not build IT projects; we build industrial-grade data engines that run silently in the background.

The Blueprint Approach (No Manual Guesswork) We never log into a cloud console to manually click buttons, open ports, or configure servers. Everything we build for you is written strictly as code. Think of it as a perfect architectural blueprint for a factory. If a system fails, we do not waste hours trying to remember how the previous developer configured it. The system simply reads our code and rebuilds the exact same environment automatically in minutes. This gives your business absolute security, eliminates human error, and ensures you are never held hostage by a single IT employee’s memory.

Industrial Reliability Over Quick Fixes Most data pipelines on the market are built quickly using loose scripting languages that fail silently when your data changes. We refuse to work that way. For our core data routing, we use strict, mathematically sound functional programming (F#). You do not need to be an engineer to understand the business benefit: our systems are designed to catch logical errors before they are deployed, not after they ruin your quarterly reports. It is the exact difference between fixing a pipe with duct tape and welding it shut. It simply does not break under pressure.

Systems That Run Themselves You should not have to pay expensive developers just to “keep the lights on.” Every piece of architecture we deliver includes automated safety nets. When we update your system, it is tested automatically. If a new piece of code has a hidden bug, the infrastructure catches it and instantly rolls back to the last safe version before your users even notice.

We engineer all the complexity out of sight. You get a boring, predictable, and unbreakable technical foundation, allowing you to focus entirely on scaling your business.

What We Actually Solve (Core Engineering Domains)

Safe Migration

Server migration to Google Cloud with zero operational downtime. Detailed infrastructure analysis and strict technical specifications ensure a stable transition without interrupting business processes.

Smart FinOps

Strict financial control over cloud expenses. Regular audits and predictive monitoring eliminate waste and optimize Google Cloud billing to prevent bill shock.

ML Engineering

Automated deployment and monitoring of machine learning models. Transformation of raw algorithms into stable, scalable production environments with ongoing technical maintenance.

Data Engineering

Native data core construction using BigQuery. Structuring and cleaning complex data streams into high-performance, accessible pipelines for advanced analytics.

1. Data Engineering (Building a Trustworthy Core) Right now, your company’s data is likely scattered across different platforms, tied together by fragile Python scripts that fail silently. When the CEO asks for a report, the numbers don’t match. We end this chaos. We architect centralized data cores in BigQuery. For the heavy lifting and complex data routing, we rely on F#—a mathematically strict language that catches errors before they ever reach your database. We build a single “source of truth” that updates instantly, never crashes, and gives your business reliable numbers you can actually base decisions on.

2. Smart FinOps (Killing the “Cloud Tax”) Cloud providers love bad code because it makes them rich. A single poorly written SQL query in BigQuery can scan petabytes of data and cost your business thousands of dollars in a matter of seconds. We treat financial optimization as a strict engineering discipline. We don’t just look at billing dashboards; we rebuild your data architecture—using smart partitioning and hard budget guardrails—so it physically cannot overspend. We turn your Google Cloud bill from a terrifying monthly surprise into a highly predictable operational cost.

3. Zero-Downtime Migration (Risk-Free Transitions) Moving your entire business infrastructure to the cloud—or upgrading a legacy system—is terrifying. The biggest fear is operational downtime: if the system goes offline, you lose revenue. We treat migration like open-heart surgery. We don’t do risky “big bang” moves. We build a parallel infrastructure in GCP and sync your data in real-time. Both systems run simultaneously until we mathematically prove the new cloud environment is perfectly stable. Only then do we flip the switch. Your users won’t even notice the transition.

4. MLOps (Taking AI Out of the Laboratory) Many companies hire expensive data scientists who build brilliant machine learning models that never actually make it into production. A model sitting on a laptop is useless. We provide the engineering bridge. We take raw algorithms and wrap them in robust, secure APIs within Google Cloud. We build the automated pipelines that constantly retrain your models as new data comes in, ensuring your AI features actually generate revenue instead of just being a cool science experiment.

Automation, Security & Support

Strict compliance with GDPR and European privacy laws within the Google Cloud environment. Data architecture is designed to guarantee absolute security and client ownership without compromising pipeline performance.

Implementation of custom CI/CD pipelines and Infrastructure as Code (IaC). Automated deployment eliminates manual operations and heavily accelerates system delivery for the final client.

Continuous infrastructure monitoring and maintenance governed by strict Service Level Agreements (SLA). Systems are supported post-deployment to ensure maximum uptime and operational stability.

When you entrust an external partner with your data architecture, your biggest risks are losing legal control of your information and being left alone when the system breaks. We architect our solutions to eliminate both risks by design.

Data Sovereignty & Strict GDPR Compliance We do not use cheap, third-party SaaS “middlemen” to route your sensitive business information. We build the entire infrastructure directly inside your own Google Cloud environment. This approach guarantees absolute data sovereignty and strict compliance with European privacy laws (GDPR). You retain 100% legal and physical ownership of your data at all times. There are no black boxes and no hidden data harvesting.

Total Transparency and Zero Vendor Lock-in Many agencies build complex systems using proprietary tools so you can never leave them. We consider that unethical. Because we build your entire environment using strongly-typed functional code (F#) and Infrastructure as Code, your architecture is fully documented and transparent. You are not renting our software; you own the codebase. If you eventually decide to hire an internal engineering department, you simply hand them the repository.

SLA-Backed Operations & Continuous Maintenance A high-performance BigQuery environment or a machine learning pipeline is only valuable if it runs reliably every single day. We do not just deploy your code and disappear. We attach strict Service Level Agreements (SLA) to our architecture. We implement automated 24/7 monitoring, cost-alert thresholds, and continuous system maintenance. If a cloud API is deprecated or a third-party data source fails, we handle the incident before it impacts your reports. You receive maximum uptime and total operational stability without the overhead of managing an internal IT support team.

Production Field Notes (Proof of Work)

We do not hide behind NDAs and corporate logos to prove our expertise. We prove it through open engineering. In our technical journal, we regularly dissect real-world high-load bottlenecks and publish the architectural blueprints we use to solve them.

Field Note #1: Silent Data Corruption in High-Concurrency Streams

  • The Problem: A high-frequency trading and logistics platform was losing exactly 2% of its critical transaction payloads during peak traffic. The monitoring systems showed green lights, the APIs returned 200 OK, but the final BigQuery ledger was missing data, causing massive financial reconciliation failures.
  • The Flaw: Schema drift handled by loosely-typed Python microservices. When the upstream payload structure mutated slightly, the interpreted scripts failed silently during transformation, dropping the payload without throwing a system-level alert.
  • The Tech-Macro Solution: We engineered a rigid “Analytics Firewall.” We ripped out the Python ingestion layer and rewrote the stream processors strictly in F#. By enforcing strong static typing and domain-driven design, any payload violating the schema is now caught at compile-time logic and instantly routed to a Dead Letter Queue (DLQ). The system now processes millions of events with mathematical guarantees of exactly-once delivery and zero silent drops.

Field Note #2: The Training-Serving Skew Catastrophe (MLOps)

  • The Problem: A predictive dynamic pricing engine performed with 94% accuracy in offline Jupyter notebook tests. However, upon deployment to production, the model began generating catastrophic pricing anomalies, costing the business hundreds of thousands of dollars in lost margins within a week.
  • The Flaw: Architecture disconnection. The data scientists trained the model on batch data in BigQuery, but the production API served predictions using a completely different, unsynchronized real-time database. The feature calculation logic drifted between the two environments.
  • The Tech-Macro Solution: We bridged the gap between raw data science and production engineering. We implemented a centralized Vertex AI Feature Store. This guaranteed that the exact same analytical transformations used during BigQuery offline training were compiled and served at low-latency for real-time inference. The training-serving skew was eliminated, stabilizing production accuracy to match the lab environment.

Field Note #3: Surviving a “Big Bang” Replatforming Outage

  • The Problem: An enterprise attempted to migrate their legacy on-premise transactional databases to GCP. They opted for a weekend “snapshot cutover.” The migration script timed out, data consistency was lost, and the business suffered a 48-hour operational blackout before frantically rolling back to the legacy hardware.
  • The Flaw: Treating infrastructure migration as a single IT task rather than a mathematical dual-write operation.
  • The Tech-Macro Solution: We took over the replatforming architecture and strictly prohibited downtime windows. We implemented a Change Data Capture (CDC) pipeline via Google Datastream, replicating legacy database changes into BigQuery in real-time. We configured a Blue-Green deployment where both the old and new systems ran in parallel. We programmatically validated the output arrays of both systems for two weeks until 100% parity was achieved. The final cutover was executed instantly without dropping a single user session.

Deep Dive into our Architecture We document our solutions, infrastructure audits, and code paradigms extensively. If you want to see exactly how we approach complex data engineering and algorithmic routing without the marketing fluff, read our technical breakdowns here: https://tech-macro.com/

Dealing with a data bottleneck? Do not worry about booking a traditional “sales call.” We do not employ salespeople. Feel free to send me an email with a detailed description of your current data setup and the exact technical or financial issues you are facing.

In return, I will asynchronously analyze your situation and reply with a custom engineering hypothesis. I will highlight potential architectural trade-offs, identify the root cause of the bottleneck, and outline a clear path to resolution. If my technical approach makes sense to your business, we can then discuss the next steps for a formal audit.