Our client is a scalable, data science-first growth engine that gives B2C teams predictive clarity into user behavior, revenue drivers, and the actions that drive sustainable growth. Fully agentic by design, our client shrinks months-long decision cycles to minutes. From Fortune 10 enterprises to beloved brands like FreshDirect, Blank Street, and Levain Bakery, run their growth on operators. THE MISSION: WIN THE TECHNICAL EVALUATION We are hiring a Solutions Architect to lead the technical side of enterprise deals. You support several account executives, carry several accounts at once, and position the company as the growth layer on top of the customer's existing data stack. As the first Solutions Architect in your region, you set how the function works. You run the technical evaluation with the prospect's data, engineering and security teams, map how the company connects to their data and advertising platforms, present the deployment patterns our product engineering team has designed, propose the one that fits, and write the solution design and feasibility assessment the proposal is built on.

What you'll do
• Own the technical strategy for your accounts from first discovery to a signed scope, ending in a documented solution design.
• Find the data access path and its owner early, before it stalls the deal.
• Own the vendor security review with a named counterpart on the customer's security team, covering encryption, anonymization and hashing, role-based access control, retention, model-training boundaries and access revocation.
• Present the company's deployment and integration patterns and explain how our ML models and agents work, spanning data ingestion, ML scoring and activation into advertising and CRM platforms, and bring modification requests to our product engineering team when a customer's setup needs one.
• Start from the potential customer's growth goal and translate growth use cases such as reactivation, acquisition quality,



cross-sell and product propensity into a data and activation design the prospect's engineers agree is buildable.
• Win the technical evaluation in competitive deals: GTM lead runs the product demo, and you run the architecture walkthrough, the live technical questions and the proof on the prospect's own data and stack.
• Turn the proof into a business case in dollars with the GTM lead, and defend it to the client side.
• Hand each signed scope to our delivery engineers and stay accountable for the design through the pilot's first measured result.
• Bring new cases and structured feedback to our engineering and product teams, and keep learning from them as the product grows.
• You are measured on technical wins, how quickly prospects get a clear feasibility answer, proof of concept to contract conversion, and how closely the pilot that gets built matches the design you wrote. Reporting and location You report to the regional GTM leader and work as the technical counterpart to the region's account executives. Their product engineering team designs the deployment patterns you present, you might adjust with slight differences and our engineering delivery team reviews every solution design before it reaches a customer and they deliver it. You are based in the region (Barcelona
- EU in this case), close to prospects, with travel of around 25 percent for Europe.

Requirements:
• Extensive experience in technical pre-sales, solutions architecture, data engineering or consulting, scoping data and ML solutions for enterprise buyers.
• The perfect mix between technical experience and customer-facing readiness mindset.




• You have carried enterprise security and architecture reviews in a regulated industry to a decision, and you know the difference between a question and a blocker. You read a customer data stack in one call: warehouse (ClickHouse, Databricks, Snowflake, BigQuery, Redshift), CDP, CRM, ad platforms and their APIs, at the level of what goes where and what it costs to move.
• Fluency in deployment and data-boundary concepts, enough to present them and spot what a customer needs changed: VPC and private link, cross-cloud connectivity, compute in place, data sharing, egress cost, data residency, SOC 2 and ISO requirements.
• Hands-on SQL, comfort reading Python, and at least one production data pipeline into a warehouse on AWS, GCP or Azure that you built or ran.
• Clear writing and compelling presentation: solution designs, data flows and interfaces an engineer can build from. You present to a CTO or a CMO as easily as to their engineers, and you say "not feasible" when it is not.
• Startup pace: several evaluations open at once, with no team to delegate to at first.
• Fluency in your region's business language (EU
- English). Pluses:
• A Databricks or Snowflake certification.
• Growth-focused data projects across industries, regulated or not, such as e-commerce, retail, fintech, telco and media.
• Experience at a CDP or ad-tech vendor, or on the customer side of an ML or CDP deployment. The Hiring Journey
• Hiring manager, 45 minutes
• Architecture session with our engineering team, 60–90 minutes: an anonymized prospect stack, which of our client's deployment patterns fits it, and what would need to change including security review questions about the design.
• Build and pitch: from a prospect brief and two call transcripts, and an anonymised dataset, produce a first analysis of the data, produce a solution design, feasibility assessment and the technical paragraph of a proposal, then present for 20 minutes.
• References: two from customers, one from a sales colleague.

📌 Pre-sales ML Services (Barcelona)
🏢 Sabia Personal
📍 Barcelona