Note: The job is a remote job and is open to candidates in USA. GOAT Group is a leading platform for authentic sneakers, apparel, and accessories, and they are seeking a Senior Data Engineer to help build and scale their data infrastructure. The role involves ensuring data reliability and compliance, building data pipelines, and collaborating with various stakeholders to translate business needs into actionable data solutions.
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)
Skills
- 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
Benefits
- 401K
- Paid time off
- Dental
- Medical
- Vision
- Disability
- Life insurance options
Company Overview
Company H1B Sponsorship