Senior Data Engineer
WFA Digital Insight
Thumbtack’s Embedded Data Engineering team sits at the intersection of product development and analytics, making this senior role uniquely hands‑on. Rather than a siloed data warehouse position, you’ll be embedded with commercial operations, shaping datasets that directly inform how the company measures and improves its home‑service marketplace. The job blends classic data‑engineer duties—building marts, maintaining feature stores, and ensuring data quality—with a push to embed data‑thinking into the software development lifecycle. Collaboration is central: you’ll partner with engineers, analysts, data scientists, and ML engineers to translate messy internal and third‑party streams into reliable, reusable data products. Candidates who can balance rigorous engineering with cross‑functional communication will thrive.
Job Description
THUMBTACK HELPS MILLIONS OF PEOPLE CONFIDENTLY CARE FOR THEIR HOMES. Thumbtack is the one app you need to take care of and improve your home — from personalized guidance to AI tools and a best-in-class hiring experience. Every day in every county of the U.S., people turn to Thumbtack to complete urgent repairs, seasonal maintenance and bigger improvements. We help homeowners know which projects to do, when to do them and who to hire from our growing community of 300,000 local service businesses. If making an impact inspires you, join us. Imagine what we’ll build together. ABOUT THE DATA ENGINEERING TEAM Thumbtack’s Data Engineering org is split into 3 groups: Core Data Engineering, Data Platform and Embedded Data Engineering. This role is on the Embedded Data Engineering team, which breaks down into multiple business units that data engineers are embedded into. This role would be on the commercial operations side of the house and will work closely with engineers, analysts, data scientists and machine learning engineers to help design and curate data sets originating from internal and third-party sources to meet current and future needs. Over the next year, it will continue to build on its prior successes in building a more cohesive data warehouse while starting to work more deeply upstream to build data best practices into the full software development lifecycle (SDLC). THE CHALLENGE There are several teams all over Thumbtack with Terabytes of data and unique challenges trying to clean and organize this data to measure their performance. In this role, you will work with Engineers, Data Scientists, Managers, and others to understand their needs, and actively work to build datasets to tackle these challenges. WHAT YOU’LL DO - Collaboratively refine and evangelize a comprehensive framework for integrating data-thinking into the software development lifecycle for product teams.
- Design, architect, and maintain core operations datasets, data marts, and feature stores that support a blend of mature products and features with a rapidly evolving product line, in partnership with analytics, data science, and machine learning.
- Integrate deeply with our cross functional partners to understand their data needs, and help design datasets with the same engineering rigor as any other software we design.
- Drive data quality and best practices across different business areas.
- Help build the next generation data products at Thumbtack, leveraging AI models for code generation and incorporating agents into our workflows.
- Excellent ability to understand the needs of and collaborate with stakeholders in other functions, especially Analytics, and identify opportunities for process improvements across teams.
- Expertise in SQL for analytics/reporting/business intelligence and also for building SQL- and Python-based transforms inside an ETL pipeline, or similar.
- Experience designing, architecting, and maintaining a data warehouse and data marts that seamlessly stitches together data from production databases, clickstream data, and external APIs to serve multiple stakeholders.
- Expertise building the above with a modern data stack based on a cloud-native data warehouse, in our case we use BigQuery, dbt, and Apache Airflow, but a similar stack is fine.
- Experience using AI to generate design plans, code and documentation as well as applying AI-enabled workflows to accelerate development velocity and improve data engineering practices.
- Strong sense of ownership and pride in your work, from ideation and requirements-gathering to project completion and maintenance.
How to Stand Out
- Highlight concrete projects where you built data marts or feature stores using BigQuery, dbt, and Airflow.
- Prepare a short portfolio of SQL queries or Python ETL scripts; include performance optimizations you implemented.
- During interviews, emphasize how you translated ambiguous business questions into clear data requirements.
- Showcase any experience using AI‑assisted tools for code generation or documentation; be ready to discuss the impact.
- Ask about the team’s current data‑quality testing framework to demonstrate your focus on reliability.
- When negotiating, factor in equity and remote‑work stipend as part of the total compensation package.
- Watch for red flags such as vague expectations around ownership or lack of clear data‑governance processes.
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