Thinkific

Data Engineer

Vaga remota de Data Engineering com fit claro de localização do candidato.

Publicada9 de jul. de 2026

Países elegíveis1 país aceito

Sinal de senioridadeMiddle

Modelo de trabalhoRemoto

Locais aceitos para candidatos

Canadá

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Países aceitos para candidatos estão listados (1).

Atualidade da fonte9 de jul. de 2026

Fit de localização1 país aceito

Match de stackGCP, LLM

Caminho de aplicaçãoSite da empresa

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Match de stackAdicione skills ao perfil para compararGCP, LLM

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Prontidão para aplicarSite da empresaA aplicação continua no site da empresa.

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Aplicando paraData EngineerThinkific

Fit de país1 país aceito

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Take ownership of undefined problems — dig in, scope the work, and ship solutions, whether that's a pipeline, a dashboard, a tracking implementation, or something we haven't thought of yet
Ship customer-facing analytics dashboards and features — this is production code, not just SQL and charts
Work directly with engineering teams to implement tracking services across our products
Interface with teams across the company who need data solutions, translating fuzzy requirements into concrete deliverables
Raise the bar on technical quality across the data team — through architecture decisions, reducing tech debt, code reviews, and bringing software development best practices to a high-performing data team
Work with your team to conduct new technology research; bring fresh ideas and concepts to bear on how we integrate AI into our data workflows

Has 3–5 years of experience working across data engineering, full-stack development, analytics, or some combination — we care more about range than a specific title
Has solid data engineering experience — you've built and maintained pipelines, worked with warehouses, and understand data modeling and quality
Has full-stack development experience — you've shipped production code that users interact with, not just internal tooling
Has hands-on experience with dbt and BigQuery or similar stacks (Snowflake, Databricks, etc.)
Is self-motivated and resourceful — you ship things, communicate clearly across technical and non-technical teams, and are always looking to level up
Is comfortable working across the stack and across disciplines; you don't need to be an expert everywhere, but you're not afraid to jump in
For reference, our current stack is primarily dbt (SQL) with Python for data work, Looker for our Analytics layer, and React, TypeScript (with some Ruby), and embedded Looker on the product side — but we're more interested in your engineering fundamentals than specific language experience.
Loves to learn and grow, They’ve found (and keep looking for) ways to level up their skills in this field, whether that’s through formal education, gaining professional experience, or maybe even building their own business

Experience with LLM/agent development — building AI-powered data products, automated workflows, or integrating LLMs into data pipelines and analytics
Experience with GCP and Terraform
Any exposure to ML
Experience white-labeling a BI tool or embedding analytics into a customer-facing product
Experience building or maintaining event tracking systems
Familiarity with modern data stack tools beyond dbt/BigQuery (Airflow, Dagster, etc.)
Experience working in startup-style environments or autonomous teams within larger orgs
Experience working in a SaaS environment

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