The position
If you are excited about growing with Hive and love operations, this might be the right position for you!
You’ll join as our first full-time data engineer, helping power our business, serve our customers better, and drive our data infrastructure.
What you’ll be responsible for:
Some exemplary topics of what we expect a data engineer to cover is below — an important note is that we’re not looking for someone that purely optimizes the technical aspects of what they’re doing, but that they’re doing it with their users (engineers, data analysts, PMs, …) and our customers in mind.
Design, develop, and maintain scalable ETL/ELT processes to support data ingestion, transformation, and storage using dbt, Python, and Redshift.
Optimize and manage our data warehouse (Redshift) to ensure high performance, reliability, and low latency.
Architect, create, and maintain data models that reflect the needs of the business.
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Develop data products to be integrated across our entire operations platform, such as:
Merchant-facing analytics for dashboards and different views in our customer-facing applications
Operational analytics for data-driven decision making in our Warehouse Management Software
Delivery time prediction models to power our "Delivery Promise" functionality across applications, from the merchant's storefront to their tracking emails and customer-facing tracking portal.
Hive is building the leading operations platform for independent commerce. It’s time for us to take the next step and deeply invest in our data platform foundations as a key enabler to enhance the value of our multi-product offering. Better commerce operations for merchants, consumers, and our fulfillment network.
What you’ll be doing:
Data pipeline optimization: Redesign, build, and optimize ETL processes to reduce latency and improve performance for real-time analytics use cases.
Scalable Data Warehouse setup: Help shape and implement a scalable data warehousing solution (e.g., in Redshift) to handle increasing data volumes and complex queries efficiently.
BI enhancement: Guide data analysts to develop new dashboards and reporting solutions in Metabase, providing actionable insights.
Data quality and consistency: Ensure comprehensive data quality and accuracy across all pipelines.
Integration with product infrastructure: Work with engineering teams to integrate data solutions with our product stack, enabling seamless data flow and enhancing our capabilities across the entire platform.