Solutions Engineer

Description

The company builds AI for marketing leaders, working alongside marketers to automate their workflows with AI agents that handle analysis, campaign planning, activation, measurement and reporting.

Customers include the NBA, Ramp, Wander, Hard Rock Stadium Group and Luxury Presence, who use the platform to understand their customers better and bring AI built around customer understanding into their commercial teams. The company builds the full stack in-house, from identity and enrichment to data integration and the agents that act on and report the analysis.

The company has raised $20M from The General Partnership, 8VC, Lingotto, NBA Investments, Topology Ventures, Future Positive, Background Capital and others. The team includes operators and investors from Citadel, Dentsu, Bridgewater, Meta Superintelligence, Growthloop, Amazon and Lazard, plus engineers and researchers from Berkeley, MIT, Stanford and Cambridge.

Each enterprise account moves from a data test to a pilot to production, and the technical win usually decides the deal. The account executive owns the deal, the account manager owns the relationship and renewal, and a forward deployed marketer owns the pilot's outcome. The solutions engineer runs the technical evaluation and gets standard accounts live. A forward deployed engineer builds what the product cannot yet do.

Custom modeling is not part of this role. Forward deployed engineers build custom pipelines and models, and you scope the hand-off. Sales is not part of it either: the account executive owns the deal, and you own whether the technical side works.

Within 90 days, you run the technical evaluation and onboarding on every assigned account without a founder present, and a first version of the onboarding playbook exists. Within 6 months, the time from signature to first scored audience has a measured figure that keeps falling, and every account handed to a forward deployed engineer goes over with a scope the customer accepted as written. Your measures are technical win rate on evaluations and time-to-live on onboardings.

Responsibilities

• Founders and data leads currently handle the technical demos and customer onboarding, and they sit on the critical path of every deal. You take over both.
• Run demos and technical evaluations, often on the buyer's own data, covering discovery with their data team, how the data moves (warehouse shares, flat files, API) and a match test on their sample.
• Own enterprise onboarding: ingest first-party data, run routine scoring from the admin dashboard and train the customer's team on the platform. For example, take a travel brand's CRM export from first file to first scored audience.
• Be the main route for enterprise customers, and know when a case needs a forward deployed engineer for custom pipelines or models, then scope that hand-off.
• Build the simple pipelines and transformations onboarding needs, in SQL and light Python.
• Handle the technical side of security reviews with the compliance lead so questionnaires don't stall deals.
• Write the playbook: standard onboarding steps, reference setups and demo environments, so the next solutions engineer ramps in weeks.
• Bring back buyer requests the product could not meet, and tell product which gaps cost deals.

Requirements

• 5+ years in solutions engineering, solutions architecture, customer data onboarding or technical consulting.
• Ideally at a martech, adtech, CDP, identity or analytics company, or in an agency's data team.
• Strong SQL, with hands-on experience of customer data and the platforms it lives in.
• Experience running a demo for a skeptical data team and an enterprise onboarding, both of which went well.
• Customer-centric: you see every problem from the customer's side first.
• Calm and clear when presenting to a room of engineers who are looking for the flaw.
• Based in New York City or willing to relocate.
• Nice to have: some data science or data engineering background, enough to have opinions on how an experiment should be set up.
• Nice to have: experience with Snowflake, BigQuery or Databricks, and how companies share data out of them.
• Nice to have: experience with consumer data, identity resolution or marketing analytics.
• Nice to have: experience in sports, financial services or travel.

Conditions

Base salary of $215,000, $241,000, depending on experience.

Work in person from the office at 300 Kent Avenue in Williamsburg, Brooklyn.

Benefits

• Variable bonus targeted at 10% of base salary.
• Meaningful equity.
• Medical, dental and vision coverage.