Senior Data Engineer

Prior to applying please note:

• We are currently unable to provide visa sponsorship for this position (including H-1B, OPT, F1, CPT or other employment-based visas). Candidates must be legally authorized to work in the United States without employer sponsorship now or in the future. 
• This role is hybrid requiring 2 days in office at our San Francisco hub every Tuesday & Wednesday (located at 130 Sutter St, San Francisco, CA). 

About the Role

We're hiring a Senior Data Engineer to build the data foundation for a new discovery-stage team focused on Taskrabbit client retention and personalization. The team's mandate is to turn our biggest unaddressed retention bet — predicting what home service a client will need and when, then reaching them proactively — from concept into validated, in-market tests. You'll be one of four new hires on a small, cross-functional pod (Product, Design, Marketing, BizOps, Machine Learning, and Engineering) reporting through Product and matrixed with Data Engineering leadership.

This is a hands-on, individual-contributor role one level below our Staff Data Engineer track: you'll own the design and build of specific data pipelines and models rather than set architectural direction for the broader platform, working closely with the team's Solutions Architect and Machine Learning Engineer as you go. It's a strong fit for someone who wants outsized ownership on a small team, is energized by ambiguity and fast iteration, and wants to help prove out (or kill) a major product bet with real evidence rather than another deck.

The ideal candidate has solid experience with modern data tools — dbt, Airflow, Snowflake (or equivalent) — and is genuinely excited to work with AI coding tools day to day. We want someone who already leverages AI (e.g., GitHub Copilot, Cursor, Claude Code) to write, test, and review code faster, and who can use that speed to move a discovery team from idea to shipped test quickly, not someone who treats AI assistance as optional or occasional.

What you will work on

• Build and maintain the data pipelines and models that capture client home profiles, job history, and seasonal or event-driven signals (weather, life events, moves) feeding a predictive personalization engine
• Partner with the team's Machine Learning Engineer and Solutions Architect to get data model-ready for predictions about what service a client will need and when
• Build the pipelines that connect personalization signals into CRM and marketing channels (email, SMS, push, onsite) so predictions show up consistently across the client experience
• Develop dbt models and semantic layers that let the team quickly stand up and measure in-market tests, such as multi-category punch cards or a recurring-category revenue model
• Use AI coding tools as a default part of your workflow — to scaffold pipelines, write tests, and speed up code review — so the team can move from hypothesis to live test quickly
• Contribute to the technical documentation and roadmap that will inform how this data foundation scales if the team's bets prove out
• Work daily with Product, Design, Marketing, BizOps, and ML partners in a small, fast-moving pod

Requirements + Areas of Expertise

• Experience building and maintaining ELT data pipelines using modern tools such as dbt, Airflow, and Fivetran
• Experience with a cloud data warehouse such as Snowflake, BigQuery, or Redshift
• Solid data modeling skills (e.g., dimensional modeling, star/snowflake schemas)
• Proficient in SQL and at least one general-purpose programming language (e