About the Data Engineering Team

Thumbtack’s Data Engineering team is a centralized team that works 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).

Challenge

This unique role will help develop and build the vision of incorporating data more fully into the SDLC at Thumbtack, while both designing and modeling datasets collaboratively with other teams, and also getting one’s hands dirty using a modern data stack. Thumbtack fosters a very collaborative culture of builders, and as such, Data Architects do not simply review data design RFCs and sign off on them, you’ll build and mentor engineers as we go.

Responsibilities

• Collaboratively refine and evangelize a comprehensive framework for integrating data-thinking into the software development lifecycle for product teams
• Design, architect, and maintain core 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 with teams consisting of product engineers, analysts, data scientists, machine learning engineers throughout the organization 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 key product and business areas
• Help build the next generation of data products at Thumbtack, based on real-time data products on top of Apache Kafka
  
  

Must-Have Qualifications

If you don't think you meet all of the criteria below but still are interested in the job, please apply. Nobody checks every box, and we're looking for someone excited to join the team.

• 8 or more years of experience designing and building data sets and warehouses
• 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 not only for analytics/reporting/business intelligence, but also in building SQL-based transforms inside an ETL pipeline
• 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
• Familiarity 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
• Strong sense of ownership and pride in your work, from ideation and requirements-gathering to project completion and maintenance

Nice-to-Have Qualifications

• Experience building ETL data pipelines in a programming language, like Python or Scala
• Experience using and/or configuring Business Intelligence tools (Looker, Tableau, Mode, et al)
• Understanding of database internals and query optimization
• Experience working with semi-structured or unstructured data in a data lake or similar
• Experience working in data engineering or a similar discipline at a two-sided marketplace or similar B2C technology company
• Experience mentoring and coaching data engineers and/or analysts
  

  For candidates living in San Francisco / Bay Area, New York City, or Seattle metros, the expected salary range for the role is currently $245,000 - $290,000. Actual offered salaries will vary and will be based on various factors, such as calibrated job level, qualifications, skills, competencies, and proficiency for the role.

  For candidates living in all other US locations, the expected salary range for this role is currently $210,000 - $250,000. Actual offered salaries will vary and will be based on various factors, such as calibrated job level, qualifications, skills, competencies, and proficiency for the role.

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