Staff Data Engineer

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) 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!

This role is Hybrid, with the expectation that employees will begin working 2 days per week in our dedicated office space in either San Francisco or in New York City (Opening in early 2026). 

About the Role

We are seeking a Staff Data Engineer to architect and lead our entire data infrastructure strategy—a rare opportunity to be the most senior data technical leader shaping how data flows through our organization. In this role, you won't just build systems; you'll define the vision, set the technical direction, and establish the standards that will guide our data practice for years to come.

Why This Role is Different

As our most senior data engineer reporting directly to the Director of Data Engineering, you'll have unusual breadth and influence to:

• Shape the Entire Data Stack: From ingestion to analytics, you'll touch every part of the data lifecycle and make foundational decisions that impact the entire business
• Be a Strategic Partner: Work directly with leadership and stakeholders across the organization to translate business strategy into data capabilities—your voice will carry weight in company-level decisions
• Build Your Legacy: Establish the patterns, practices, and culture that will define how we work with data. Your architectural choices and mentorship will shape the team's future
• Wear Multiple Hats: Dive deep into technical challenges one day, mentor engineers the next, and present strategy to executives the third—the variety keeps the work engaging and your skills sharp

The ideal candidate has deep expertise with modern data platforms (dbt, Airflow, Snowflake or equivalents), strong data modeling and orchestration skills, and a track record of building production systems that scale. Just as importantly, you're an exceptional communicator who can engage technical and non-technical stakeholders alike, you thrive on ownership and cross-functional collaboration, and you're energized by the opportunity to continue building out data architecture.

What You'll Work On:

• Design, build, and maintain scalable, reliable data pipelines and infrastructure to support analytics, operations, and product use cases
• Develop and evolve dbt models, semantic layers, and data marts that enable trustworthy, self-serve analytics across the business
• Collaborate with non-technical stakeholders to deeply understand their business needs and translate them into well-defined metrics and analytical tools
• Lead architectural decisions for our data platform, ensuring it is performant, maintainable, and aligned with future growth
• Build and maintain data orchestration and transformation workflows using tools like Airflow, dbt, and Snowflake (or equivalent)
• Champion data quality, documentation, and observability to ensure high trust in data across the organization
• Mentor and guide other engineers and analysts, promoting best practices in both data engineering and analytics engineering disciplines

Role Requirements:

• 7-10 years of experience in Data Engineering
• Expertise in building and maintaining ELT data pipelines using modern tools such as dbt, Airflow, and Fivetran
• Deep experience with cloud data warehouses such as Snowflake, BigQuery, or Redshift
• Strong data modeling skills (e.g., dimensional modeling, star/snowflake schemas) to support both operational and analytical workloads
• Proficient in SQL and at least one general-purpose programming language (e.g., Python, Java, or Scala)
• Experience with streaming data platforms (e.g., Kafka, Kinesis, or equivalent) and real-time