GOAT Group

Senior Data Engineer

Rol remoto de Senior Data Engineer con fit claro de ubicación del candidato.

Publicado19 jul 2026

Países elegibles41 países aceptados

Señal de senioritySenior

Modelo de trabajoRemoto

Ubicaciones aceptadas para candidatos

AlbaniaArmeniaAustriaBielorrusiaBélgicaBulgaria+35 más

Resumen del rol

Contenido del rol extraído en secciones para revisar más rápido.

Own the reliability, availability, and accuracy of our data infrastructure, ensuring compliance with data privacy standards
Build, maintain, and improve our data pipeline using our modern data stack (Snowflake, DBT, Airflow, Looker)
Build centralized, durable, and reusable data models that serve the broader business, built with AI and ML consumption in mind
Build and maintain a semantic layer with canonical dimension and metric definitions
Partner closely with Analysts, PMs, Engineers, Marketing, Legal, Fraud, and other stakeholders, translating business needs into actionable data solutions
Build Reverse ETL pipelines in Python; use and build APIs to move and expose data
Proactively bring in new data sources, improve existing data models, and establish SLAs/SLOs for key business dependencies
Champion data governance and help elevate the organization's data maturity — establishing standards, closing gaps, and ensuring data is handled responsibly at every layer
Own data feeds that support in-product marketing, retention communications, and Growth Marketing feeds (i.e. Google Shopping, Meta, Tiktok, etc)

5+ years of engineering experience, with a strong foundation in software and data engineering, and familiarity with analytics engineering
Experience with our stack: Snowflake, DBT, Airflow, Fivetran, Segment, Looker, Amplitude, Algolia, AWS/Lambda, Git
Deep experience building, maintaining, and architecting data pipelines end-to-end, ingestion through consumption
Strong SQL skills and proficiency in Python
Experience building and consuming APIs

Has driven projects from scoping through delivery, owning the outcomes and not just the tasks, comfortable sitting with ambiguity but driven to resolve it
Treats data quality and observability as first-class concerns, comfortable implementing monitoring and alerting practices that catch issues before the business does
Builds with the team in mind, systems that are intuitive, approachable, and easy for others to understand, extend, and maintain
Pragmatic about technology choices, the goal is solving business problems, not chasing the latest tools
Comfortable operating with a high degree of autonomy, self-directed and able to drive work forward, while keeping the team informed and aligned along the way
Strong communicator, equally effective with engineers and non-technical stakeholders
Naturally elevates the data literacy of those around them by educating and guiding stakeholders toward better data practices

Familiarity with ML/data science workflows and how data engineering supports them
Exposure to search and discovery platforms
Prior experience in retail, resale, or marketplace commerce, genuine curiosity about the domain a must regardless

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