{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Data Matas","title":"S2E5 - From Learning the Tool to Designing the System: How Engineers Actually Grow","html":"","width":"100%","height":180,"duration":1986,"description":"How do analytics engineers grow from writing SQL to designing entire data systems? And why do most companies still confuse tool mastery with real engineering skill?In this episode of Data Matas, Oleg Agapov (Senior Analytics Engineer at Hiive) shares what it actually takes to go from junior to senior in data—beyond bootcamps, tools, and job titles. He breaks down the core shifts that matter most: thinking in systems, mastering data modelling, and creating structure that scales.You’ll hear how Oleg is helping build self-serve analytics inside a fast-moving fintech startup, why most data work fails without discovery, and how AI is changing the role—but not replacing the role—of the analytics engineer.🎙 Guest: Oleg Agapov, Senior Analytics Engineer at HiiveOleg has spent over 15 years in data roles, moving from analyst to engineer to analytics architect. Now at Hiive—a marketplace for private stock—he’s helping design scalable data models and BI tooling that enable business teams to self-serve. Oleg also mentors junior engineers and shares career guidance on LinkedIn weekly, offering a rare combination of technical depth and practical coaching.⏱ Episode Takeaways & Timestamps03:40 – Why analysts become engineers (and what tools don’t teach you)Why Oleg moved from analytics into engineering, and how messy data triggered a career pivot.08:15 – What junior vs senior actually looks like in analytics engineeringFrom DBT basics to architecture thinking—how your role shifts as you grow.12:30 – Data modelling isn’t a feature, it’s a disciplineWhy writing queries isn’t enough—and why most engineers only realise this at scale.17:45 – Building analytics in a three-sided marketplace startupHow Oleg is helping Hiive build self-serve data for a unique financial model.24:00 – How AI fits into the modern data workflow (and where it fails)Why LLMs are better reviewers than creators—and why trust still starts with humans.28:40 – The hidden risk of AI assistants in BI toolsWhat...","thumbnail_url":"https://img.transistorcdn.com/ct59eQJqdPsuicw7AJDtS5q80LLSqi7Wq51tbBc6kq4/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9kMDUw/MDJhMDFlMGQ5Yzdk/ZjY2MzAxZjFlODVm/MGU3Yy5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}