Senior Data Engineer

Taskrabbit•San Francisco, CA

3d•$130,000 - $170,000•Hybrid

About The Position

Taskrabbit is seeking a Senior Data Engineer to join a new discovery-stage team focused on client retention and personalization. This role is crucial for building the data foundation to predict client needs and proactively reach them. The position is a hands-on, individual-contributor role, one level below Staff Data Engineer. The ideal candidate will own the design and build of specific data pipelines and models, working closely with a Solutions Architect and Machine Learning Engineer. This role is a strong fit for someone who thrives in ambiguity, enjoys fast iteration, and wants to make a significant impact on a small, cross-functional team. The team is part of a hybrid work model, requiring two days a week in the San Francisco hub.

Requirements

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, not a mature, fully-scoped platform.
Candidates must be legally authorized to work in the United States without employer sponsorship now or in the future.

Nice To Haves

Familiarity with BI or semantic-layer tools such as Looker, Mode, or Tableau.
Experience with streaming platforms such as Kafka or Kinesis — this team's predictions run on event-driven triggers (weather events, life events), so comfort with real-time data processing is a strong plus.

Responsibilities

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.