Staff Data Engineer

Taskrabbit · San Francisco, California, United States

  • Senior
  • Full-time
  • $150,000 – $200,000
  • Posted 2026-08-18
  • Confirmed live on 25 September 2026

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Job description

About Taskrabbit:

Taskrabbit is a marketplace platform that conveniently connects people with Taskers to handle everyday home to-do’s, such as furniture assembly, handyman work, moving help, and much more.

At Taskrabbit, we want to transform lives one task at a time. As a company we celebrate innovation, inclusion and hard work. Our culture is collaborative, pragmatic, and fast-paced. We’re looking for talented, entrepreneurially minded and data-driven people who also have a passion for helping people do what they love. Together with IKEA, we’re creating more opportunities for people to earn a consistent, meaningful income on their own terms by building lasting relationships with clients in communities around the world.

Taskrabbit is a hybrid company with employees distributed across the US and EU and a Built In — Best Places to Work (2022, 2023, 2024, 2025) continually ranked across multiple national and regional categories. Join us at Taskrabbit, where your work will be meaningful, your ideas valued, and your potential unleashed!

Prior to applying please note:

• We are currently unable to provide visa sponsorship for this position (including H-1B, OPT, F1, CPT, H4 or other employment-based visas). Candidates must be legally authorized to work in the United States without employer sponsorship now or in the future. H-1B transfers are valid on a case by case basis.

• 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 Staff 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.g., Python, Java, or Scala)

• Regularly use AI coding assistants (e.g., Copilot, Cursor, Claude Code) in your day-to-day work, and know how to prompt, review, and validate AI-generated code rather than just accept it

• Comfortable with ambiguity — this is a discovery-stage team proving out new bets,

Prepare for the interview

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