Google Cloud consulting built around real production work
DEV3LOPCOM, LLC is an Austin, Texas consultancy, 100% onshore, and Google Cloud is where much of our recent pipeline work lives. We built GCP data pipelines for Buxton — you can read that engagement in our Buxton case study — and we bring the same stack to every project: BigQuery, Cloud Run, Cloud Functions, GKE, Pub/Sub, Dataflow, Cloud Composer, Cloud Storage, and Cloud SQL, provisioned with Terraform and deployed through Cloud Build or GitHub Actions.
GCP tends to attract data-heavy teams, and that suits us. Our practice grew out of analytics consulting — pipelines with trillions of records at Nielsen, streaming dashboards for ExxonMobil — so BigQuery cost control and pipeline reliability are the problems we solve most often.
How an engagement runs
Assess. We review your billing export in BigQuery (the single most useful thing GCP gives you for free), your project and IAM structure, and how workloads actually run. On-demand query spend, always-on GKE clusters serving batch jobs, and unlifecycled Cloud Storage buckets are the usual suspects.
Design. We spell out trade-offs instead of defaults. Cloud Run over GKE unless you genuinely need Kubernetes — most teams do not. BigQuery on-demand versus slot reservations has a real crossover point, and we compute it against your query history. Dataflow when you need streaming semantics; a Cloud Run job on a scheduler when you honestly do not.
Build. Infrastructure as code from day one, environments split by project, service accounts scoped to least privilege, CI/CD as the only deploy path. Pipelines get monitoring and alerting before they get more features.
Hand off. Documentation, cost dashboards, and working sessions with your engineers. You keep the repo, the runbooks, and the understanding.
Problems we fix on GCP
- BigQuery bills growing faster than the business — fixed with partitioning, clustering, materialized views, and moving hot dashboards off raw tables.
- Pipelines that only one person can restart. We rebuild them on Composer or Cloud Run with retries and alerting; see our data pipeline consulting services for how we approach that work.
- Lift-and-shift VMs on Compute Engine that should be Cloud Run services — smaller, cheaper, and autoscaled to zero.
- IAM granted at the project level with primitive roles. We move to predefined roles and dedicated service accounts without a big-bang breakage.
- Analytics stuck in silos: operational data in Cloud SQL, events in Pub/Sub, and no warehouse joining them. We land it all in BigQuery with clear modeling so BI tools have one honest source.
Why dev3lop
Our founder is a former Tableau Software Professional Services consultant, and we have spent years making infrastructure serve analytics rather than the other way around. We hold no Google partner tier and resell no licenses — you pay for engineering and get engineering. Teams running more than one cloud can lean on us for AWS and Azure as well.
Get a straight answer
Tell us what hurts — the BigQuery invoice, the pipeline that fails on Sundays, the migration you have scoped three times. We will tell you what we would do and what it costs. Contact us to start.