Founding Senior Data Engineer, Domains Search at Squarespace

New York, New York, United States -

Full Time

Start Date

Immediate

Expiry Date

15 Apr, 26

Salary

231500.0

Posted On

15 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, Machine Learning, Natural Language Processing, Data Pipelines, SQL, Cloud Platforms, Feature Engineering, Data Quality Monitoring, Schema Design, Debugging, Batch Processing, Streaming Data, Low-Latency Systems, Feature Stores, Vector Databases, Search Systems

Industry

Software Development

Description

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. You'll Get To... 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. Who We're Looking For 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. Benefits & Perks A choice between medical plans with an option for 100% covered premiums Fertility and adoption benefits Access to supplemental insurance plans for additional coverage Headspace mindfulness app subscription Global Employee Assistance Program Retirement benefits with employer match Flexible paid time off 20 weeks for parental leave and up to 12 weeks to care for an ill family member Pretax commuter benefit Education reimbursement Employee donation match to community organizations 8 Global Employee Resource Groups (ERGs) Dog-friendly workplace Free lunch and snacks Private rooftop Hack week twice per year Cash Compensation Range: $128,500 - $231,500 USD The base salary for this position will vary based on job-related criteria including relevant skills, experience, and location, among other factors. In addition to the cash compensation above (which includes base salary and, where applicable for eligible roles, may include overtime pay), Squarespace employees are eligible to be granted an option to purchase our common stock. Sales positions generally offer a competitive On Target Earnings (OTE) incentive structure in addition to base salary. About Squarespace Squarespace is a design-driven platform helping entrepreneurs build brands and businesses online. We empower millions of customers in more than 200 countries and territories with all the tools they need to create an online presence, build an audience, monetize, and scale their business. Our suite of products range from websites, domains, ecommerce, and marketing tools, as well as tools for scheduling with Acuity and creating and managing social media presence with Bio Sites and Unfold. Our team of more than 1,500 is headquartered in bustling New York City, with offices in Dublin, Ireland, Aveiro, Portugal, and coworking spaces in the UK, Netherlands, and Australia. For more information about our company culture, visit https://www.squarespace.com/about/careers. Our Commitment Today, more than a million people around the globe use Squarespace to share different perspectives and experiences with the world. Not only do we embrace and celebrate the diversity of our customers, but we also work toward the same in our employees. At Squarespace, we are committed to equal employment opportunity regardless of race, color, ethnicity, ancestry, religion, national origin, gender, sex, gender identity or expression, sexual orientation, age, citizenship, marital or parental status, disability, veteran status, or other class protected by applicable law. We are proud to be an equal opportunity workplace. #LI-Hybrid Thank you in advance for providing the following details about your work history from your resume! This helps us ensure that your candidate information is accurate and consistent during the hiring process. Squarespace will never solicit your personal banking information or ask you to transfer money in connection with a job offer or interview. We also will not reach out to you via phone or SMS without your permission or knowledge.

Responsibilities

Build and own the data pipelines that power the search system, including corpus ingestion and index refresh. Collaborate with Machine Learning Engineering and Data Science to ensure clean, reliable data for experimentation and evaluation.