Analytics Engineer

Taskrabbit•San Francisco, CA

2d•Hybrid

About The Position

Taskrabbit's Data Engineering team is looking for a Senior Analytics Engineer to build and maintain the data models that power decision-making across the company. You'll sit at the intersection of data engineering and analytics — turning raw data into trusted, well-documented datasets that business teams rely on every day. This is a hands-on, individual-contributor role: you'll own key parts of the data model layer end to end, partnering closely with data engineers, analysts, and business stakeholders to keep our metrics consistent and our warehouse trustworthy.

Requirements

2+ years of experience in analytics engineering, data engineering, or a related role, with deep hands-on experience in dbt and SQL
Strong understanding of dimensional modeling and data warehouse design (Snowflake, BigQuery, or Redshift experience preferred)
Experience with orchestration tools (e.g., Airflow, dbt Cloud) and version control (Git)
Working knowledge of Python for data pipeline development and automation
A track record of translating ambiguous business questions into clear, well-structured data models
Strong communication skills — able to explain technical tradeoffs to both engineers and non-technical stakeholders
Experience mentoring other engineers or leading technical projects

Nice To Haves

Familiarity with BI or semantic-layer tools such as Looker, Mode, or Tableau is a plus
Experience with marketplace or on-demand business models is a plus

Responsibilities

Design, build, and maintain scalable dbt models that transform raw data into clean, tested, well-documented datasets
Own key parts of the data model layer, from source to mart, ensuring consistency in business logic and metric definitions across the warehouse
Partner with data engineers, analysts, and business stakeholders to understand reporting needs and translate them into reliable data pipelines
Establish and enforce testing, documentation, and code review standards for dbt projects
Monitor data quality and freshness, and troubleshoot discrepancies when numbers don't match across reports
Improve query performance and warehouse efficiency as data volume grows
Mentor junior analytics engineers and contribute to team best practices and tooling decisions
Help define and maintain a single source of truth for core business metrics (e.g., GMV, completed tasks, take rate)