The Data Analytics & BI team is pioneering a data-driven intelligent platform to optimize and grow E-commerce businesses. Our mission is to design advanced automation, decision-support tools and autonomous algorithms. We believe that human-machine collaboration is the key to capture new trends before they happen, deploy effective quantitative marketing strategies and best tailor our products to our customers’ needs. To achieve this vision, our Tech team relies on cutting-edge approaches to data and software engineering enabling us to quickly iterate on new ideas and confidently turn them into data-driven products.
BRANDED’s business model will expose you to a number of unique challenges: collecting and analysing data on thousands of Amazon sellers to find acquisition targets, integrating new datasets resulting from acquisition of new business, synchronizing different operational systems, analyzing data trends to identify opportunities for operational improvement, conducting A/B testing for product listing optimization, analyzing brands’ operational metrics (sales, costs, revenues, etc.) to identify opportunities for improvement, analyzing consumer behavior from Direct To Consumer (DTC) websites. The roadmap is ambitious, and we are growing extremely fast.
In this role you will be building the data foundation to make this vision a reality. Our analytical stack is evolving to be GCP, Airbyte, Airflow, BigQuery, dbt, Looker. If you are looking to join a successful e-commerce company as an early member of the data team and make an impact, this is it!
- Develop, maintain and refactor scalable data models & pipelines that allow the business to get maximum value from our datas, with high quality, clean, maintainable analytic code
- Curate, organize and document data definitions, metrics and reporting environments across raw datasets, dbt, and Looker to unlock value and drive efficiency while ensuring data quality
- Modeling data and writing documented, maintainable ETL pipelines to provide datasets to answer key analytical questions
- Owning the quality of the data being delivered to the reporting and analytics layer - including writing integration and unit tests, monitoring for data issues, debugging data inconsistencies.
- Selecting and incorporating tooling that gives monitoring and observability to the data pipeline
- Performing exploratory data analysis, onboarding and integration of new datasets
- Help define and improve our internal standards for architecture, style, maintainability, and best practices for a high-performance data organization