Senior Data Engineer

Grailed•Los Angeles, CA

2d•$108,800 - $160,000

About The Position

Grailed is looking for a Senior Data Engineer to help us build and scale the data infrastructure that powers one of the world's leading fashion resale marketplaces. Our data sits at the center of a complex, peer-to-peer marketplace and touches nearly every part of the business. This is a hands-on role for a seasoned engineer who takes pride in reliable, well-crafted systems, and who is energized by working closely with a tight-knit team of data partners, engineers, Product Managers, Marketers, and executive leadership who depend on the data every day to make decisions and build great products. The strongest candidates will have enough breadth of experience across different roles and environments to know that building it right matters as much as building it at all.

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
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

Nice To Haves

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

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)