Staff Data Engineer – Personalization & AI Pipelines

TaskRabbit; San Francisco (CA); Hybrid; USD 150,000 - 200,000; Full time

Benefits offered by this job

Employer-paid health insurance

401(k) match with immediate vesting

Generous time off

Product stipends

IKEA discounts

Reproductive health support

Wellness stipend

Job summary

Taskrabbit is hiring a Staff Data Engineer in San Francisco to build data foundations for a discovery-stage retention/personalization initiative. You will own data pipelines and models, collaborating with ML, product, design, and BizOps teams to move swiftly from hypothesis to live tests.

The role emphasizes hands-on implementation with modern tools (dbt, Airflow, Snowflake/BigQuery/Redshift) and a strong focus on AI-assisted development. Hybrid in-office presence required.

Qualifications

• 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
• 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
• 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 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
• Use AI coding tools as a default part of your workflow — to scaffold pipelines, write tests, and speed up code review
• 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

Skills

ELT pipelines

SQL

Python

Scala

AI coding assistants

Tools

dbt

Airflow

Fivetran

Job description

Taskrabbit is hiring a Staff Data Engineer in San Francisco to build data foundations for a discovery-stage retention/personalization initiative. You will own data pipelines and models, collaborating with ML, product, design, and BizOps teams to move swiftly from hypothesis to live tests.

The role emphasizes hands-on implementation with modern tools (dbt, Airflow, Snowflake/BigQuery/Redshift) and a strong focus on AI-assisted development. Hybrid in-office presence required.