Data engineer in AI platforms

We’re building a new category - an intelligent marketing system that transforms how companies grow. One and a half years in, we’re growing 20% month-over-month, just closed a $6M seed round, and are used by 100+ brands that know what good looks like. We were recently named one of Wired’s 100 Hottest Startups and included on Sifted’s Startups To Watch.

• Own the data platform end-to-end - ingestion from diverse sources, transformation, quality assurance, and delivery at scale, making deliberate infrastructure choices that compound over time
• Build and maintain the graph-based semantic layer that bridges unstructured marketing signals with structured, queryable data that is continuously enriched and updated
• Establish and enforce data quality standards - schema validation, anomaly detection, and monitoring that catches problems before customers do
• Work directly with the founders, AI engineers, and product teams to ensure the data layer enables fast iteration on new features and agent capabilities
• Close the AI-data loop - keep data fresh and accurate so our agents learn from the best signals, and use AI at the core of the data infrastructure itself
• Help shape our engineering culture and raise the bar as the team grows - through code review, architectural decisions, and how we work together

• Strong experience building and operating production data infrastructure - pipelines, storage, quality systems, and the tooling around them
• Experience with modern data engineering patterns - data mesh, data vault, data quality, and data governance - not classic ETL and BI warehouse architectures
• Strong proficiency in Python and modern data tools (e.g. Dagster, Polars, DuckDB, Apache Iceberg, Apache Arrow, ClickHouse, Kafka)
• An eye for data quality and scale as a first-class engineering problem, not an afterthought
• Strong foundations in CS or Engineering, though exceptional candidates with alternative backgrounds are welcome
• Будет плюсом: Experience with data infrastructure for AI systems - evaluation pipelines, data flywheels, or MLOps - is a strong plus; Experience with streaming architectures and real-time data pipelines is a plus

• Meaningful early-stage equity, zero bureaucracy, and the kind of impact you only get before everyone else shows up.
• Competitive salary, comprehensive health coverage, hybrid work in Lisbon with flexible hours, and top-of-the-line equipment.