Architect, maintain and optimize scalable data pipelines to deliver critical insights for Skims using Fivetran, DBT, Airflow, Snowflake, BigQuery and various AWS services.
Assemble large and complex data sets that meet functional and non-functional business requirements.
Maintain and contribute to our data quality efforts using DBT tests and Monte Carlo.
Collaborate with data scientists, analysts and other stakeholders to understand data requirements and deliver tailored solutions.
Document technical designs, workflows and best practices to facilitate knowledge sharing across the data team and other engineering teams.
Support process failures when necessary and serve in an on-call rotation.
Requirements
3+ years of experience in a data engineering role, preferably working in a direct-to-consumer business.
Excellent SQL skills and experience using Python to manipulate data and draw insights from large datasets required.
Experience creating dashboards and reporting using visualization tools like Looker (preferred), Tableau, or PowerBI required.
Demonstrated ability to independently plan and execute complex data engineering pipelines for large datasets.
Ability to translate business strategies into technical requirements.
Creative thinker, adept at understanding a wide breath of retail analytics use cases and identifying the data and analytics necessary to make them into a reality.
Direct experience with the data platform stack currently in use at SKIMS highly desirable: Fivetran, AWS Lambdas, dbt, Snowflake, BigQuery, Looker, Airflow.
Demonstrated success working in a fast-paced, swiftly changing startup environment.
BA/BS in Math, Computer Science, Analytics, Data Science, or another quantitative field desirable.
Benefits
100% Company Paid Healthcare (medical, dental, vision)
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