Senior Product Manager - Technical

Responsibilities

• Surface ownership. Own an area of the product from the problem through to the outcome: the case for doing the work, the requirements engineering builds from, and the usage it shows six months after release.
• Requirements and written decisions. We work in documents. A requirements document states the problem, the decision taken, what is in scope and what is not, and how success will be measured. Presentations are for reporting, not for deciding.
• Scope discipline. A rebuild invites reproducing every feature that exists today. Establish what is actually used, make the case for retiring the rest, and hold that position in review.
• Release management. Select the customers who receive each release first, define the test that closes a milestone, and report the result as it stands.
• New product development
• Opportunity identification. Attributes that have never been packaged, audiences that have never been priced, and questions customers are currently paying somebody else to answer.
• Early validation. A test with two or three customers, a measure agreed in advance, and a short written finding. Evidence before a long build rather than after it.
• Applied AI. Determine where a model or an agent belongs in the product and where it does not, specify how its output will be evaluated, and demonstrate the idea with a working prototype rather than a description of one. Design for consumption by machines as well as by people: predictable interfaces, described data, and output that a system downstream can act on without a person in between.
• Progression or discontinuation. Both are acceptable outcomes. An idea that remains a proposal for three quarters is not.

Requirements

• AdTech or MarTech product experience. Required. You have built and released product within an advertising or marketing technology business: audience data, a demand or supply side platform, a customer data platform, an identity or onboarding provider, a clean room, or measurement.
• Understanding of identity data, online and offline. Deterministic and probabilistic matching, first party data onboarding, the difference between reaching a person through a mail file and through a bid request, the effect of an identifier being withdrawn, and the constraints privacy regulation places on all of it.
• Exposure to both consumer and business marketing , or a demonstrated understanding of how the second differs from the first.
• Sound judgement at speed. The cadence here is fast, which is not a licence to guess. Assess the options, state the trade off, choose, and be able to explain what would change the decision. A decision taken in a day on seventy percent of the information, and revised when the remainder arrives, is worth more than a month spent reaching certainty.
• A product taken from proposal to paying customer. A new product, a new line, or a feature nobody requested that proved to matter. Be ready to describe how it was tested before it was built.
• Data fluency without being a data engineer. Strong SQL, familiarity with Snowflake, Databricks or an S3 and Parquet data lake, and the ability to validate coverage and match rates before a figure is published.
• AI fluency. Required. What we build has to work in an AI first world, consumed by agents as readily as by people, so this is not a preference. You use current AI tools in your own work, for analysis, prototyping and writing. You can judge where a model or an agent belongs in a product and where it does not. And you understand what a model changes about a requirement: how its output is evaluated, how it fails, and what the underlying data has to look like for it to work at all.
• Written clarity. Plain sentences, a stated position, visible reasoning. A writing sample will be requested and read closely.
• A record of stopping work. A feature, a roadmap item or a project already under way. Be ready to describe what it cost and how the decision was carried.
• Distributed working. Part of engineering is based in India. Most collaboration is written, and it succeeds only when the writing carries the detail.

Preferred

• Advantageous and not required: direct mail or other offline channels, data distributed through the Snowflake or Databricks marketplaces, clean rooms, or a company built by acquisition.

Benefits

• Flexible Time Off (FTO) and Company Holidays
• Medical, Dental, Vision, and Health Savings Account (HSA) benefits
• 401(k) with Company Match
• Company-Paid Life and AD&D Insurance
• Short-Term and Long-Term Disability Insurance
• Voluntary Life & AD&D Insurance
• Critical Illness and Accident Insurance