01
Evidence before adjectives
I prefer a qualified metric, pilot boundary, or concrete delivery artifact over a broad claim that cannot be defended.
About
Product decisions get better when technical reality is visible early.
I’m a Technical Product Manager with a hands-on software engineering background. My work spans B2B analytics, launch and monetization, enterprise delivery and technical acceptance, data products, automation, and AI/NLP research.
My role is not to be the engineer in the Product seat. It is to understand the user and business problem, make the product decision explicit, translate it into executable requirements, work deeply with engineering, and measure what actually happened.
Technical depth improves that judgment: feasibility becomes less abstract, API and data constraints surface earlier, acceptance criteria get sharper, and engineering conversations lose less meaning in translation. Discovery, prioritization, user context, and business value still lead the decision.
Product
WHYWhy should we build this?
Problem · Users · Value · Priority · Metrics · Go-to-market
×
Technology
HOWHow can this realistically be built?
Architecture · APIs · Data · Constraints · Acceptance · Operations
The intersection
Technical Product Management
Product judgment with enough technical depth to make delivery and measurement more realistic.
How I work
Operating principles that show up in the work.
02
Specification is product work
Detailed flows, acceptance criteria, event semantics, edge cases, and role clarity are how strategy becomes executable.
03
Analytics should change a decision
Funnels, cohorts, attribution, and event taxonomies matter when they help choose what to improve, stop, or test next.
04
Technical depth reduces translation loss
APIs, SDKs, databases, ETL, AI systems, and infrastructure context help me collaborate with engineering without pretending Product and Engineering are the same job.
05
Launch is a learning mechanism
When more polish is blocking evidence, a controlled launch or pilot can be the highest-leverage product decision.
06
Maturity labels matter
Research, prototype, controlled field test, operational use, and production are different states and should be communicated differently.
Technical depth
Useful when it improves product decisions.
Python, PHP and JavaScript; ETL and scraping; MySQL and ClickHouse context; REST APIs, webhooks, SDK and Telegram integrations; TensorFlow, PyTorch, Hugging Face, NLP and computer vision; Docker, Scrapy and Git. I also use Figma at a working level for scenarios, user flows, and screen-level communication — not as an advanced visual-design claim.