Data Engineering & Advanced Analytics
Data silos kill innovation. We build modern, governed, and scalable data pipelines that transform terabytes of raw data into strategic intelligence in real-time.
Data-Driven Transformation
From Data Lake to Executive Dashboard, we handle the entire information lifecycle.
Modern Data Engineering (ELT)
We replace fragile ETLs with robust ELT pipelines, using tools like dbt, Airflow, and Snowflake for scalable cloud processing.
Data Lakehouse
The union of Data Lake flexibility with Data Warehouse management. Store structured and unstructured data in a unified platform.
Governance & Quality
Implementation of Data Catalogs, Lineage, and automated quality controls to ensure data is reliable and GDPR compliant.
AI-Ready Infrastructure
We prepare your database for Artificial Intelligence. Feature Stores and clean pipelines so your data scientists can innovate, not clean data.
Data Observability
Data drift, anomalies and freshness monitored: the dashboard that breaks warns before breaking.
Streaming when needed
Kafka and Kinesis for the cases that demand low latency: fraud, personalization, real-time operations.
From raw data to decision: the Lakehouse flow
Every layer with an owner, a contract and verified quality. No spreadsheet in the middle of the path.
The 5 layers of the modern pipeline
Ingestion
Sources connected in batch and streaming (CDC, Kafka, Kinesis) without fragile point-to-point links.
ELT
Transformation where the data lives, version-controlled in SQL with dbt and orchestrated by Airflow.
Lakehouse
Bronze, silver and gold: complete history, reliable tables and storage cost under control.
Governance
Catalog, lineage, RBAC and PII masking: LGPD applied at the data layer, not in the report.
Consumption
BI, executive dashboards and feature stores for AI, all drinking from the same trusted source.
From spreadsheet to Lakehouse without a big bang
Every phase delivers usable data value, starting with the domain that hurts the most.
Data discovery
Inventory of sources, owners and pains. We prioritize the business domain where bad data costs the most.
Pipeline foundation
Ingestion, storage and orchestrator in place, with data contracts and quality tests from day one.
Modeling and consumption
dbt layers, versioned metrics and dashboards the business actually uses. Catalog adopted along the way.
Continuous governance
Lineage, RBAC, PII masking and data observability as routine. LGPD evidenced, not promised.
Data FAQ
Extracting value from information.
Data Warehouse stores structured and processed data for reporting. Data Lake stores raw data of any type. We often implement "Lakehouse" to get the best of both.
We use data observability tools that monitor "data drift", anomalies, and pipeline failures, alerting engineering before the CEO's dashboard breaks.
We implement granular access controls (RBAC), data masking (PII masking), and direct access auditing at the data layer, ensuring full compliance.
Yes. We build streaming architectures using Apache Kafka or Kinesis for use cases requiring zero latency, such as fraud detection or live personalization.
That is the typical case. Ingestion connects sources gradually, and the catalog becomes the single map. We start with the highest-value domain.
Not necessarily. We assess what you already have; the Lakehouse comes in when the cost or flexibility of the current setup blocks the business.
Talk to Data Engineering
Describe your data scenario and get a pipeline, lakehouse and governance proposal.
Quick conversation.
Address
Avenida Paulista, 1636 - São Paulo - SP - 01310-200