Senior Data Engineer

GOAT Group is a leading platform for authentic sneakers, apparel, and accessories, and they are seeking a Senior Data Engineer. The role involves building and scaling the data infrastructure that supports their fashion resale marketplace, ensuring data reliability and compliance while collaborating with various stakeholders.

Responsibilities:

• 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)

Requirements:

• 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
• 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)
• 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