Staff Data Platform Engineer

Thumbtack

3d•$212,500 - $323,400•Remote

About The Position

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 Platform Team The Data Platform team builds the shared systems, primitives, and services that make data reliable, discoverable, and actionable across Thumbtack. We build and own the systems that enable ingesting, warehousing, transforming, and actioning on data in real-time and batch, as well as the developer experience for data consumers and producers—enabling analytics, ML, and product teams to move quickly with confidence. The Challenge Our marketplace demands high-quality, timely data across product and marketing; the platform must serve both heavy analytical workloads and low-latency real-time use cases. In this role you’ll own systems design and the platform’s architectural direction — defining APIs and data contracts, setting SLIs/SLOs, and trading off latency, cost, and long-term maintainability. You’ll have real agency over the roadmap: identify gaps with product/ML/analytics partners, prioritize investments, and own cross-cutting initiatives from scope to delivery. You’ll coach engineers in systems thinking, run design reviews, and raise standards through tooling and feedback. Success looks like durable platform primitives, faster dataset onboarding, and measurably more trusted data.

Requirements

8+ years of experience designing, building, and scaling data systems—spanning pipelines, warehouses, and analytical data products that drive measurable business impact.
Proven technical leadership in architecting and evolving complex data ecosystems, including ownership of data models, transformation frameworks, and integrations across multiple data domains; advanced proficiency in SQL and Python.
Deep experience with modern data stacks, including cloud-native warehouses (e.g., BigQuery or Snowflake), orchestration tools (Airflow or equivalent), and transformation frameworks (dbt or similar).
Strong system design and architecture mindset, able to reason about scalability, cost, and performance trade-offs, and define long-term data strategy.
Exceptional collaboration and influence skills, partnering effectively with Marketing Engineering, Analytics, and Data Science to translate business goals into robust, production-grade data systems.
Strong sense of ownership and accountability, balancing hands-on technical execution with the ability to mentor others, raise standards, and drive organization-wide improvements in data quality and reliability.
functional counterparts (design, product, data science).
Commitment to building inclusive teams and team culture.

Responsibilities

Define platform architecture. Design and evolve shared platform services (data ingestion, orchestration, transformation, metadata, and observability) that balance scale, cost, and operational simplicity.
Build core infrastructure. Implement and own platform features (e.g., transformation frameworks, feature stores, real-time ingestion pipelines, lineage and observability) that power analytics and ML.
Drive data quality & observability. Champion observability, governance, and data quality standards so every dataset is trustworthy and measurable.
Enable developers. Improve the developer experience for data producers and consumers—tooling, templates, docs, and CI/CD for data assets.
Lead through influence. Mentor platform and product engineers, evangelize best practices across teams, and partner with analytics, ML, and product to prioritize platform investments.
Balance tradeoffs. Make pragmatic architecture decisions and articulate tradeoffs between speed, reliability, and maintenance cost.

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