Sisifo Analytics builds analytics platforms on Google Cloud for companies that want fast answers from large volumes of data. We centralize your sources in BigQuery, automate the pipelines that keep it current and put the results in dashboards and machine learning models your teams act on. Our Miami-based team of Latin American specialists works with clients in Latin America, the United States and Europe.
What can Sisifo build for you on Google Cloud?
Google Cloud analytics revolves around BigQuery, so our designs start there and add only what each use case requires.
- BigQuery data warehouse: partitioned, documented tables that answer business questions in seconds, even across years of history.
- Batch and streaming pipelines: Dataflow, Pub/Sub and Cloud Composer jobs that bring in ERP, CRM, web and app data on time.
- SQL transformations: Dataform models that turn raw tables into trusted metrics, with version control and tests.
- Marketing and product analytics: Google Analytics 4 and advertising data joined with sales data inside BigQuery.
- Reporting: Looker Studio, Looker or Power BI dashboards on top of BigQuery.
- Vertex AI and Gemini: forecasting and segmentation models and generative AI applications built on your own data.
How do we deliver a Google Cloud data project?
Our approach balances quick wins with a foundation that lasts, so the first dashboard is never a dead end.
- Scoping: agree on the questions to answer, the sources involved and how success is measured.
- Landing the data: connect sources to Cloud Storage and BigQuery through automated pipelines.
- Modeling: define shared metrics so every team counts revenue, customers and orders the same way.
- Activation: deliver dashboards and models where people already work.
- Continuous improvement: review query costs, data quality and adoption, then plan the next phase.
Why do companies trust Sisifo with Google Cloud work?
Our engineers have shipped 50+ data and AI projects in more than 10 industries and bring 18+ years of combined experience. We pair that track record with a Miami base and a team spread across Latin America, which keeps communication simple for clients in the Americas and Europe.
“Sisifo’s customer segmentation and recommendation tools helped us reassign coverage so our sales reps stopped ‘shooting blind’ and could focus their time where it matters. We reduced each seller’s portfolio from ~600 clients to ~150–175 by moving low-value accounts to call-center support. That freed the team to spend more time with the customers worth visiting. Highly recommend Sisifo if you want a partner that brings innovative ideas to the table and pushes you to new frontiers.”
Daniel Garcia, COO
Is Google Cloud the right platform for your data?
Google Cloud stands out when analytics speed matters, when you rely on Google Analytics and Google Ads data, or when you want serverless tools with little infrastructure to manage. If your company runs on Microsoft or Amazon services, see our Azure and AWS pages. Snowflake and Databricks are also available on Google Cloud.
Frequently asked questions
Why is BigQuery popular for analytics?
BigQuery is serverless, so there is no cluster to size or maintain, and it can scan billions of rows in seconds. You pay for storage and the queries you run, which suits teams that want to start small.
Can you bring Google Analytics 4 and Google Ads data into BigQuery?
Yes. GA4 and Google Ads both export to BigQuery. We combine that data with your CRM and sales records so marketing spend can be tied to real revenue.
Looker Studio, Looker or Power BI: which should we use?
Looker Studio is free and fits simple reporting. Looker adds a governed semantic layer for larger organizations. Power BI is often the better option if your company already uses Microsoft 365. All three connect to BigQuery.
How do you keep BigQuery costs under control?
We partition and cluster large tables, avoid queries that scan unnecessary columns and set quotas and budget alerts. For heavy, steady workloads we evaluate capacity-based pricing.
Can you build AI solutions on Google Cloud?
Yes. We use Vertex AI for forecasting and segmentation models, and Gemini models for assistants that answer questions over your company data.
What happens in the first meeting?
Book a meeting and we will talk through your data sources, the reports you rely on and what you want to improve. You leave with a suggested first phase and the Google Cloud services it would use.
Turn your data into answers on Google Cloud
Share your current sources and the questions your team wants answered first.
