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, 2025) 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!Prior to applying please note:We are currently unable to provide visa sponsorship for this position (including H-1B, OPT, F1, CPT, H4 or other employment-based visas). Candidates must be legally authorized to work in the United States without employer sponsorship now or in the future. H-1B transfers are valid on a case by case basis.This role is hybrid requiring 2 days in office at our San Francisco hub every Tuesday & Wednesday (located at 130 Sutter St, San Francisco, CA). About the RoleWe're hiring a Staff Data Engineer to build the data foundation for a new discovery-stage team focused on Taskrabbit client retention and personalization. The team's mandate is to turn our biggest unaddressed retention bet — predicting what home service a client will need and when, then reaching them proactively — from concept into validated, in-market tests. You'll be one of four new hires on a small, cross-functional pod (Product, Design, Marketing, BizOps, Machine Learning, and Engineering) reporting through Product and matrixed with Data Engineering leadership.This is a hands-on, individual-contributor role one level below our Staff Data Engineer track: you'll own the design and build of specific data pipelines and models rather than set architectural direction for the broader platform, working closely with the team's Solutions Architect and Machine Learning Engineer as you go. It's a strong fit for someone who wants outsized ownership on a small team, is energized by ambiguity and fast iteration, and wants to help prove out (or kill) a major product bet with real evidence rather than another deck.The ideal candidate has solid experience with modern data tools — dbt, Airflow, Snowflake (or equivalent) — and is genuinely excited to work with AI coding tools day to day. We want someone who already leverages AI (e.g., GitHub Copilot, Cursor, Claude Code) to write, test, and review code faster, and who can use that speed to move a discovery team from idea to shipped test quickly, not someone who treats AI assistance as optional or occasional.What you will work onBuild 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 enginePartner 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 whenBuild the pipelines that connect personalization signals into CRM and marketing channels (email, SMS, push, onsite) so predictions show up consistently across the client experienceDevelop 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 modelUse 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 quicklyContribute to the technical documentation and roadmap that will inform how this data foundation scales if the team's bets prove outWork daily with Product, Design, Marketing, BizOps, and ML partners in a small, fast-moving podRequirements + Areas of ExpertiseExperience building and maintaining ELT data pipelines using modern tools such as dbt, Airflow, and FivetranExperience with a cloud data warehouse such as Snowflake, BigQuery, or RedshiftSolid 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 itComfortable with ambiguity — this is a discovery-stage team proving out new bets, not a mature, fully-scoped platformFamiliarity with BI or semantic-layer tools such as Looker, Mode, or TableauExperience 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.Compensation & Benefits: At Taskrabbit, our approach to compensation is designed to be competitive, transparent, and equitable. Total compensation consists of base pay + bonus + benefits + perks. The base pay range for this position is $150,000 - $200,000. This range is representative of base pay only, and does not include any other total cash compensation amounts, such as company bonus or benefits. Final offer amounts may vary from the amounts listed above and will be determined by factors including, but not limited to, relevant experience, qualifications, geography, and level.You’ll love working here because:Taskrabbit is a Hybrid Company. We value flexibility and choice but also stay committed to regular in-person connection.The People. You will be surrounded by some of the most talented, supportive, smart, and kind leaders and teams -- people you can be proud to work with!The Diverse Culture. We believe that we make better decisions when our workforce reflects the diversity of the communities in which we operate. Women make up half of our leadership team and our diversity representation is above that of the tech industry average.The Perks. Taskrabbit offers our employees with employer-paid health insurance and a 401k match with immediate vesting for our US based employees. We offer all of our global employees generous and flexible time off with 2 company-wide closure weeks, Taskrabbit product stipends, wellness + productivity + education stipends, IKEA discounts, reproductive health support, and more. Benefits vary by country of employment. Taskrabbit’s commitment to Diversity and Inclusion:An Active Commitment to Equity within our Company and Platform. We are an inclusive community where all who share our mission and values belong. Our diverse team represents the communities we serve, breaking down systemic barriers, and transforming lives- one action at a time.Taskrabbit is an equal opportunity employer and values diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, ancestry, citizenship, sex, gender, gender identity, sexual orientation, age, marital status, military/veteran status, or disability status. Taskrabbit is committed to working with and providing reasonable accommodation to applicants with physical and mental disabilities. Taskrabbit will consider for employment all qualified applicants with criminal histories in a manner consistent with applicable law. AI-Assisted Prescreening Notice [US Based Candidates Only]: As part of our hiring process, we may use artificial intelligence tools to assist with the initial prescreening of applications and responses. This tool does not make hiring decisions — every application and response is reviewed by a member of our recruiting team to determine fit for the role. If you would prefer not to have your application processed using this tool, you may opt out by selecting 'opt out' in the application form or by emailing View email address on click.appcast.io, and your application will be reviewed manually with no impact on your candidacy.