⚡ Data Engineering
Robust & scalable pipelines
Design, deployment and monitoring of ETL/ELT pipelines to centralize and transform your data in real time. From source to report in seconds.
Pipeline running
LIVE
► airflow dags trigger pipeline_prod
Ingesting 2.4M records...
dbt run --models staging+
✓ 47 models completed in 43s
Loading to Snowflake...
✓ Pipeline completed successfully
Architecture
Which type of pipeline?
ETL — Extract Transform Load
Data is transformed before being loaded. Ideal for warehouses with strict schemas and complex business rules.
ELT — Extract Load Transform
Raw data is first loaded, then transformed in the warehouse (dbt). Perfect for Snowflake, BigQuery and Redshift.
Real-time streaming
Kafka, Flink or Spark Streaming for cases requiring sub-second latency: fraud, alerts, live dashboards.
Batch + Micro-batch
Orchestrated by Airflow or Prefect for scheduled loads (nightly, hourly) with error recovery and dependencies.
Best Practice
Medallion Architecture
Data stratification into increasingly refined layers, from raw to analytics.
1
Sources
ERP, CRM, API, CSV files — all your data sources
2
Bronze
Raw ingested data as-is — no transformation
3
Silver
Cleaned, deduplicated and validated data — ready for analysis
4
Gold
Aggregated and modeled data for KPIs and dashboards
5
Consumption
BI, ML, reports — your teams access business-ready data
In production
A pipeline in action
Continuous monitoring, automatic alerts and error recovery. Your pipelines run while you sleep.
[$] airflow trigger_dag --dag_id etl_clients
INFO Starting run 2026-06-08T02:15:00
INFO Ingesting Salesforce → Bronze layer...
✓ 847,293 records loaded
INFO Running dbt models (Silver)...
✓ 32 models, 0 errors, 0 warnings
INFO Aggregating KPIs (Gold layer)...
✓ Dashboard updated — 14 tables refreshed
⏱ Total: 2m 43s | SLA: 5m ✓
Stack
Our data engineering stack
Apache Spark
Apache Kafka
Airflow
dbt
Fivetran
Stitch
Databricks
Snowflake
BigQuery
AWS Glue
Azure Data Factory
Prefect
Great Expectations
dlt
PostgreSQL
Real cases
Use cases
E-commerce
Multi-source centralization
Pipeline unifying Sage ERP, HubSpot CRM and WooCommerce store to a Snowflake Data Warehouse — reports in 2 minutes instead of 3 hours.
Finance
Real-time reporting
Kafka streaming of bank transactions with automatic alerts. Anomaly detection in < 500ms.
Enterprise
Cloud migration
Migration of an on-premise Oracle data warehouse to BigQuery with zero downtime and automatic rollback.