Data Engineering
We design the full analytics value chain, from ingestion to decision-making.
From data to decisions
We connect scattered sources, organize them into reliable models and turn them into dashboards and indicators your team uses to decide.
We also work on quality and interoperability: cross-matching records between agencies, validation, cleansing and source consolidation, the foundation every AI project needs.
Let's talk about your dataWhat's included?
- Ingestion: ELT/ETL pipelines, APIs, streaming and batch processing
- Modeling: data warehouses, data lakes and lakehouse architectures
- Activation: dashboards, executive BI and embeddings for AI
- Decision: indicators and reports for decision-making
- Quality and interoperability: record matching, validation, cleansing and consolidation
- Geospatial data and indicator platforms
Technologies and frameworks
- Power BI
- Tableau
- Databricks
- Airflow
- QGIS
- Qualtrics XM
Data engineering FAQ
Where does a data project start?
With the decisions you need to make. From there we identify the sources, assess their quality, define the minimum architecture needed and deliver a first useful dashboard before scaling.
Which tools do you use?
Power BI and Tableau for dashboards and executive BI; Databricks and Airflow for pipelines and processing; QGIS for geospatial data; and Qualtrics XM for surveys and experience management.
Can you integrate data from several agencies or systems?
Yes. We design interoperability models and record matching, validation and cleansing processes to consolidate sources that do not talk to each other today.
How do data and AI relate?
Useful AI depends on reliable data. We prepare the data and build the pipelines and embeddings that feed solutions such as RAG assistants or classification models.
Next step
Is your data helping you decide?
Tell us what you need to measure and we will propose the architecture and the first indicators.
Book a consultation