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Hedgehog Lab in the United Kingdom is seeking a Solution Architect, Data (GCP / AWS) to own the data architecture for a high-volume online marketplace during a major transformation.
The role focuses on optimizing the GCP stack (BigQuery, DBT, Data Stream) and guiding migration toward AWS, with cost-aware data models, self-service analytics, and a strategic data vision.
Solution Architect, Data (GCP / AWS)
Contract, outside IR35. Starting as soon as possible.
You'll own the data architecture for a high-volume online marketplace, part-way through a major, multi-year technology transformation.
The current stack runs on Google Cloud Platform: BigQuery for storage and consumption, DBT for transformations, and Data Stream. Reporting is on Looker today, and moving the business toward a self-service reporting model is a core goal.
There's real architecture work in front of you. The business doesn't yet have a defined data strategy, and BigQuery consumption costs are climbing, so optimising data models and building in cost management is a live priority, not a future one.
The wider platform is migrating to AWS, and the data estate moves with it. The near-term focus is the current GCP stack, optimising models and cutting BigQuery cost, but you'll be architecting toward the AWS target state from the start rather than just maintaining what exists.
You'll also shape structural change. The data team currently sits under Finance and is fragmented, with a plan to bring it under technical leadership, and data scientists are being introduced on the seller side to support seller attraction and analytics. A larger data transformation is expected further down the line.
This is a genuinely large-scale marketplace. Very few platforms handle a product catalogue or transaction volume at this level.
The roadmap includes agentic AI workflows built into the platform, retail media integrated directly into the marketplace, and infrastructure work targeting cost and performance at high volume.
A lot of this is being attempted at volumes that have rarely been tested in a marketplace before, so the work leans research-led rather than routine.
This is a fast-paced scale-up environment on a complex platform.