About HIVED
HIVED is the first customer‑centric, tech‑enabled parcel delivery company designed for e‑commerce. With a 100% electric fleet and commitment to an exceptional delivery experience, HIVED is setting a new delivery standard for e‑commerce brands and consumers.
HIVED has already delivered millions of parcels for brands such as Nespresso, H&M, Zara and John Lewis and is a B Corp certified business. Backed by leading investors such as Maersk Growth, NordicNinja, and Planet A Ventures, HIVED is leading the industry by making great service and green delivery the standard, building out a delivery network that’s fit for the future.
Senior Data Engineer
We are looking for an ambitious, curious and versatile Senior Data Engineer to drive business impact through a robust and flexible data platform. Our ideal future team mate cares deeply about delivering a world‑class, reliable data platform and developer experience that supercharges the work of analysts, analytics engineers and data scientists across the company.
As a Senior Data Engineer, you will work in a hybrid role: while you will help your squad meet its goals, your primary mandate is the long‑term integrity and scalability of our core data infrastructure. Your day‑to‑day activities will range from ideating and implementing core data platform features, creating a strategic vision and guidance for our data platform and development experience all the way to delivering squad‑level features that leverage data to meet squad goals (e.g. sophisticated reverse‑ETL solutions). This is a highly collaborative role, embedded in a squad with team mates from Data, Tech and Product but also collaborating across squads with other analytics engineers or data scientists to empower their work. This role offers the opportunity for line management or technical mentoring of fellow analytics engineers but doesn’t strictly require it.
Responsibilities
* Data platform ownership and vision: define and execute the technical roadmap for our core data platform, ensuring highest levels of reliability, observability, data quality and flexibility.
* Build for self‑service: design and implement tooling that empowers Analytics Engineers and Data Scientists to build their own pipelines and models safely and autonomously, removing yourself as a bottleneck.
* Leverage our data: drive business impact within your squad through sophisticated ways of operationalising data in our products.
* Raise the bar: Lead through influence and collaborate with fellow Data team members to grow and improve our analytics and machine‑learning & AI platform and set technical standards across the organisation.
Your profile
* 5+ years of experience in a similar data engineering, analytics engineering or backend engineering role. You have a proven track record of building and scaling data systems in production environments.
* Highly proficient in Python and able to write maintainable, testable, and efficient code. You are also a SQL expert who understands how to optimise complex queries and design resilient data models.
* Experience or a deep interest in managing "Data Infrastructure as Code." We lean heavily on open‑source solutions like Dagster and self‑host on GCP via Kubernetes. Familiarity with these, or the ability to navigate K8s environments, is a massive plus.
* Comfortable within the Google Cloud (GCP) ecosystem (or similar) and have experience with data warehouses like BigQuery and cloud storage patterns.
* You advocate for CI/CD, automated testing, and observability. You believe that a data platform should be treated with the same discipline as a core product.
* A clear and bold communicator who knows how to “scale themselves” through precise documentation and written guides while also enjoying direct collaboration across squads.
Technologies we use
* Programming languages: Python, SQL
* Core data platform tools and frameworks: Dagster, dlt and Airbyte, dbt, data‑diff, Elementary
* Data lake and warehouse: BigQuery, Iceberg + BigLake
* Infrastructure: Cloud Run, Cloud Storage, GKE (Google Kubernetes Engine), Terraform
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