Sofia-based I Technical Product Manager

About us

Infinite Lambda is Europe’s fastest-growing data and AI consultancy. We empower leading companies to scale and become data-first through agile, cloud-native solutions and cutting-edge practices.

Our mission is to bring top professionals together to make an impact at the intersection of tech and sustainable growth.

Headquartered in London and having distributed teams based in the UK, France, Hungary, Slovakia, Bulgaria, Vietnam, Singapore, Italy, Romania and now expanding to South America we embrace diversity and make sure everyone is happy to be their true self at work. We have already started building teams in Brazil, Argentina, and Chile, and are looking forward to welcoming more teammates to contribute to meaningful projects. Together, we can grow, learn, and experiment with the cutting-edge data and AI technology (dbt and Snowflake among others) that we are all excited about.

About the role

Flowline moves enterprises off legacy ETL platforms onto a modern data stack. Coding agents write most of our code, so our pace depends on how quickly we decide what to build and how clearly we define “done”. This role owns both. This is an individual contributor role with no direct reports. Product marketing sits outside it.

Mission: Decide what Flowline builds next and why, test ideas with working prototypes before engineering commits, and take the product from hands-on enterprise migrations to customers who can migrate on their own.

• Anticipated start date: In Dec 2026
• Location: Sofia (Bulgaria), hybrid
• Interview process

1. Intro call (with TABP): 45-min
2. Take-home exercise: up to 4 hours to complete
3. 1st Interview (with Product team): 90-min
4. 2nd Interview (with Customer success team): 45-min
5. Cultural Chat (with Executive member): 45-min

What you’ll do

• Talk to customers, delivery teams and sales every week. Turn what you hear into a ranked roadmap, and say no to the rest with a reason.
• Set up the feedback loop with Flowline’s three user groups: our forward-deployed engineers, Infinite Lambda’s consulting engineers and client teams. Ask for candid criticism and show people what changed because of it.
• Prototype ideas yourself with AI tools and put them in front of users before engineering builds anything. Use the prototype to settle the debate.
• Write the definition of done for each feature in plain language, with examples of good and bad output. Engineers and coding agents should be able to test their work against it.
• Build small tools and automations that save the team time, such as feedback triage, usage reports and release notes. We built our own roadmap planner this way and expect the same instinct here.
• Own onboarding so a new user gets value without a call: in-product guidance, documentation, and a demo video generated automatically for every release.
• Own the path to self-serve, so that smaller customers can run a migration on their own and pay for what they use.
• Give sales clear product answers and demos, and use usage data to inform pricing and packaging decisions.
• Review AI-assisted work before it reaches a customer, and take responsibility for it once it ships.

How we’ll measure it

• Milestones delivered on the agreed date against their written definition of done.
• Share of roadmap items with an agreed definition of done before build starts.
• Days from customer feedback to a decision on it.
• Time from a new user’s first login to their first migrated pipeline.
• Share of shipped features with a demo video and release note within a week of release.
• After self-serve launches, the share of small customers who complete a migration without our help.
• In the first 90 days: one milestone delivered against its definition of done, and a feedback forum that FDEs and consulting engineers use.

What we’re looking for

• 5+ years in product management in B2B software, or in software engineering, data engineering, solutions or customer engineering with a move into product. You have taken a product from its first customers to repeatable use.
• You build with AI tools such as Claude, Cursor or similar, and can show us something you built and put in front of real users.
• You write specs that leave little room for interpretation, and you can define “good” so it can be tested.
• You spend real time with customers and turn what you learn into decisions.
• You are comfortable with numbers and run your own analysis instead …