Petyo Pahunchev
Chief Product Officer @Infinite Lambda
Signup · Get unlimited contacts
WORK HISTORY
Chief Product Officer @Infinite Lambda
London, GB
Lead product development for Flowline, Infinite Lambda\'s agent-native legacy ETL modernisation platform.Flowline automates conversion of Informatica PowerCenter, Talend, and SSIS systems to dbt + Fivetran. The platform combines deterministic parsing (99% PowerCenter keyword coverage) with LLM-based AI agents that resolve edge cases, undocumented transformations, and non-standard mappings. A graph based dependency maps full lineage before code generation, making each conversion intelligently sequenced.Current product priorities include extending agent coverage for remaining edge cases and scaling delivery for enterprise engagements in regulated industries (insurance, financial services, life sciences) with mapping counts in the thousands.Also responsible for Infinite Lambda\'s broader product portfolio, go-to-market strategy, pricing models, and sales enablement. Working cross-functionally with Marketing, Sales, Engineering, and Strategic Consulting.Author of Data & AI: Fast and Slow.
EDUCATION
The University of Manchester
"Bachelor of Science", Computer Software Engineering
SKILLS
ABOUT PETYO PAHUNCHEV
I lead product at Infinite Lambda, where my primary focus is Flowline, our agent-native legacy ETL modernisation platform. Flowline converts Informatica PowerCenter, Talend, and SSIS workflows to dbt + Fivetran architectures using deterministic parsing for the majority of constructs and LLM-based agents for edge cases, non-standard mappings, and undocumented logic. The system builds a full dependency graph before generating target code, so conversions are auditable and testable rather than black-box.Before data and AI, I spent 8 years in financial services engineering at Morgan Stanley (FX options, repo trading, prime brokerage) and JP Morgan (UK payments). That regulated-industry background shapes how I approach AI systems: deterministic where possible, auditable throughout, agents only where rule-based approaches fall short.Technical Advisor at a Stealth AI Start Up for financial services. Author of Data & AI: Fast and Slow.Current interests: agentic AI for code migration, context engineering for domain-specific LLM tasks, and the practical gap between legacy data infrastructure and AI readiness.
This profile is compiled from publicly available professional sources. Unifers is not affiliated with or endorsed by LinkedIn. Request removal of this profile.