BigQuery specialists for your dedicated team.

Where BigQuery fits in a long-term product team.

The serverless model removes most operational work and replaces it with a design discipline. A query selecting every column from an unpartitioned table is correct, fast enough, and costs many times what the same result needed. Partitioning and clustering are the two decisions that most affect the monthly figure.

BigQuery is frequently the default for teams already in Google Cloud, particularly where analytics data arrives from GA4 or Firebase. That convenience is real, and it makes it more likely the warehouse grows without a clear owner for its shape.

What your dedicated team does with BigQuery.

Because BigQuery charges for bytes scanned, its cost curve is set by decisions made once, early, by whoever happened to create the table. Partitioning chosen in week two determines the bill in year two, and by then changing it means rewriting everything that reads from it.

When the analytics estate has grown without an owner, the first job is usually an inventory, and when the work turns out to be modelling, it moves to a dedicated data science team.

Common BigQuery use cases.

• Analytics data at event granularity Raw event exports queried directly, with full control over how data is aggregated.
• Cutting query spend Partitioning, clustering and materialised results applied to the queries that run most, identified from real usage.
• A warehouse without a platform team Central analytical storage for an organisation that has no appetite to operate a cluster.

Adding BigQuery skills to your team.

BigQuery work is assigned in a dedicated data engineering team, working in your own project and billing account.

BigQuery specialists join your team remotely as dedicated, full-time team members, selected, employed and managed by us in Chiang Mai and Bangkok. Companies that hire offshore developers through Azendo add the skill at the next monthly cycle.