Founding Senior Data Engineer, Domains Search

Squarespace•New York, NY

30d•Hybrid

About The Position

The Domains Search team at Squarespace is building the next generation of intelligent search experiences for helping customers find the perfect domain. From leveraging large language models to generate creative domain suggestions, to building scalable ranking and personalization systems, our team sits at the intersection of applied machine learning, natural language processing, and large-scale search & recommendation infrastructure. We're looking for a Data Engineer to build the data foundation that powers our search and ML systems. You will report to the Engineering Manager of the Domains Search team in our New York City headquarters. Why Join Us This is a unique opportunity to shape a greenfield ML initiative at Squarespace. As part of a small, high-impact team, you'll have influence over how we build our search platform and how millions of customers discover their online identity. If you've wanted to build a next generation of search system with few legacy constraints, we'd love to hear from you.

Requirements

6+ years of professional experience in data engineering, including 2-3+ years supporting ML or search systems.
Experience building and operating batch and streaming data pipelines at scale on a major cloud platform (AWS, GCP, or Azure). Background in low-latency data systems is a strong plus.
Strong SQL skills with modern data tooling (Spark, Airflow, dbt, or similar).
Familiarity with ML infrastructure, such as feature stores, vector databases, embedding pipelines and ideally search systems (Elasticsearch, Solr, Vespa, or similar)
Data ownership to drive quality end-to-end, including schema design, validation, monitoring, and debugging.

Responsibilities

Build and own the data pipelines that power our search system: corpus ingestion, processing, enrichment, and index refresh.
Design and maintain feature pipelines for ML models, partnering with our Ranking MLE on feature engineering and computation.
Own the embedding pipeline infrastructure to run our models at scale.
Integrate with and extend feature store infrastructure to serve features at training and inference time.
Establish data quality monitoring, validation, and alerting across the search data stack.
Partner with Machine Learning Engineering on index refresh strategies and schema evolution.
Collaborate with Data Science to ensure clean, reliable data for experimentation and offline evaluation.
Make pragmatic infrastructure decisions. You’ll adopt existing systems where they fit and build new ones where they don't.