Senior Data Engineer

Taskrabbit•San Francisco, CA

2d•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 the Staff Data Engineer track. 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 use data to validate product hypotheses. The role requires significant experience with modern data tools like dbt, Airflow, and Snowflake, and a strong desire to leverage AI coding tools daily for increased efficiency in writing, testing, and reviewing code.

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.
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 — comfort with real-time data processing is a strong plus.

Responsibilities

Build and maintain data pipelines and models for client profiles, job history, and seasonal/event-driven signals feeding a predictive personalization engine.
Partner with Machine Learning Engineer and Solutions Architect to prepare data models for predictions on client service needs and timing.
Build pipelines connecting personalization signals to CRM and marketing channels (email, SMS, push, onsite) for consistent client experience.
Develop dbt models and semantic layers for rapid setup and measurement of in-market tests (e.g., multi-category punch cards, recurring-category revenue model).
Use AI coding tools as a default workflow component for scaffolding pipelines, writing tests, and speeding up code review.
Contribute to technical documentation and roadmap for scaling the data foundation.
Work daily with Product, Design, Marketing, BizOps, and ML partners in a small, fast-moving pod.