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# Senior Data Engineer

Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move.

[Apply for this job](#job-application)[View company](https://jobicy.com/company/thumbtack.md)Share1 Sep 2026Published29Listing views2Application actions1 Oct 2026Apply before  Opportunity details

## About this role.

AI SummaryThumbtack is hiring a Senior Data Engineer for its Embedded Data Engineering team supporting commercial operations. The role will design and maintain core operational datasets, data marts, feature stores, and warehouse integrations across production, clickstream, and third-party data. This engineer will partner closely with analytics, data science, machine learning, and product engineering stakeholders while promoting data quality and SDLC-oriented data practices. The position requires strong SQL and Python transformation skills plus experience with modern cloud data tooling such as BigQuery, dbt, and Apache Airflow. The role also emphasizes practical use of AI-enabled workflows for design, code generation, documentation, and improved engineering velocity.

## Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

### Job Complexity

4/5EasyHard

### Pace & Pressure

4/5RelaxedFast-paced

### Autonomy Level

4/5GuidedFull ownership

### Communication Load

5/5IndependentCollaborative

AI insightThis is a senior-level, cross-functional data engineering role requiring architecture ownership across complex, high-volume data sources and multiple stakeholder groups. Success depends on both deep technical execution and the ability to establish scalable data practices across product teams.

## Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job medianHighly competitive$226,100US market range$179k–$273k0$300k

AI insightThe employer explicitly discloses location-based annual USD salary ranges from $179,400 to $272,800. The midpoint across the full disclosed range is $226,100 yearly; actual pay depends on the candidate's US location, calibrated level, qualifications, skills, competencies, and proficiency. The disclosed range is an appropriate US market range for a senior data engineering role at a technology company.

## Core skills

Skills and capabilities most closely associated with this opportunity.

[Data Engineering](https://jobicy.com/jobs?search_keywords=Data%20Engineering.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[Data Warehousing](https://jobicy.com/jobs?search_keywords=Data%20Warehousing.md)[ETL/ELT](https://jobicy.com/jobs?search_keywords=ETLELT.md)[BigQuery](https://jobicy.com/jobs?search_keywords=BigQuery.md)[dbt](https://jobicy.com/jobs?search_keywords=dbt.md)[Apache Airflow](https://jobicy.com/jobs?search_keywords=Apache%20Airflow.md)[Data Modeling](https://jobicy.com/jobs?search_keywords=Data%20Modeling.md)[Data Quality](https://jobicy.com/jobs?search_keywords=Data%20Quality.md)

Sample interview questionsHow would you design a data mart that combines production database data, clickstream events, and third-party APIs for commercial operations reporting?I would begin by defining the core business entities, reporting metrics, grain, refresh requirements, and ownership with stakeholders. I would ingest each source into governed staging layers, standardize identifiers and event timestamps, implement tested transformations in dbt, and publish dimensional or domain-oriented marts with documented lineage. I would also establish freshness, completeness, and reconciliation checks before exposing the mart to downstream analytics users.

Describe how you have used dbt and Airflow together in a production data platform.

I have used Airflow to orchestrate ingestion, dependency management, retries, alerting, and scheduled execution, while dbt managed SQL transformations, model dependencies, documentation, and data tests. A typical workflow loads raw data, runs dbt models in dependency order, executes source and model tests, and sends alerts when freshness or quality checks fail. This separation keeps orchestration and transformation logic maintainable and observable.

What practices would you introduce to incorporate data-thinking into the software development lifecycle?

I would add data requirements to product design and technical planning, including event definitions, ownership, identifiers, schema contracts, privacy considerations, and success metrics. I would establish a lightweight review process for instrumentation and schema changes, version important events, and require automated validation before release. Clear documentation and shared accountability between product engineers and data teams are essential for adoption.

How do you ensure data quality when multiple teams own upstream systems and datasets?

I use explicit data contracts, named owners, and observable service-level expectations for freshness, volume, schema stability, and key business metrics. Automated tests should validate uniqueness, referential integrity, accepted values, and reconciliation against source systems. When issues occur, clear alert routing, incident playbooks, lineage, and post-incident improvements help prevent repeated failures.

How would you responsibly apply AI tools to data engineering work?

I would use AI to accelerate drafts of SQL, Python, documentation, test cases, and design proposals, but retain engineer review for correctness, security, performance, and business logic. Generated code should go through the same version control, peer review, automated testing, and deployment processes as manually written code. I would avoid exposing sensitive data to unapproved tools and measure whether AI workflows meaningfully improve quality or delivery speed.

### 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

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Collaboratively refine and evangelize a comprehensive framework for integrating data-thinking into the software development lifecycle for product teams.

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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.

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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.

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Drive data quality and best practices across different business areas.

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Help build the next generation data products at Thumbtack, leveraging AI models for code generation and incorporating agents into our workflows.

### In order to be successful, you must bring

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4+ years of experience designing and building data sets and warehouses.

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Excellent ability to understand the needs of and collaborate with stakeholders in other functions, especially Analytics, and identify opportunities for process improvements across teams.

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Expertise in SQL for analytics/reporting/business intelligence and also for building SQL- and Python-based transforms inside an ETL pipeline, or similar.

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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.

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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.

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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.

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Strong sense of ownership and pride in your work, from ideation and requirements-gathering to project completion and maintenance.

### Expected salary ranges

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For candidates living in San Francisco / Bay Area, San Jose, New York City, or Seattle metros, the expected salary range for the role is currently $210,800.00 – $272,800.00.

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For candidates living in Austin, TX or Washington DC metros or in California, Massachusetts, New Jersey, or Washington states, the expected salary range for the role is currently $189,600.00 – $245,300.00.

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For candidates living in all other US locations, the expected salary range for this role is currently $179,400.00 – $232,100.00.

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.

Thumbtack embraces diversity. We are proud to be an equal opportunity workplace and do not discriminate on the basis of sex, race, color, age, pregnancy, sexual orientation, gender identity or expression, religion, national origin, ancestry, citizenship, marital status, military or veteran status, genetic information, disability status, or any other characteristic protected by federal, provincial, state, or local law. We also will consider for employment qualified applicants with arrest and conviction records, consistent with applicable law.

Thumbtack is committed to working with and providing reasonable accommodation to individuals with disabilities. If you would like to request a reasonable accommodation for a medical condition or disability during any part of the application process, please contact: [recruitingops@thumbtack.com](mailto:recruitingops@thumbtack.com).

For information about how Thumbtack collects, uses, and shares personal information about job applicants, please see our [Job Applicant Privacy Policy](https://www.thumbtack.com/job-applicant-privacy-policy).

We put as much craftsmanship into candidate safety as we do into the hiring experience itself. While scammers may try to impersonate our team, we’ll never ask you for money, banking info, or SSNs during hiring. [Check out our blueprint on how to spot the fakes.](https://careers.thumbtack.com/recruiting-scams)

Show more

[Apply now >](https://jobicy.com/jobs/152318-senior-data-engineer-3.md)

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