EXPERTISE

Data Engineering

Is your data siloed, unreliable, or unusable? We build and strengthen your data foundations—including your architecture, pipelines, and governance—so your teams can finally rely on high-quality data, backed by the right tools, the right skills, and solid data engineering practices.

The finding

Without a solid data foundation, all your analytics and AI projects are built on sand. Poor data quality, fragile pipelines, and silos between systems—these issues hold back the entire organization and delay value creation.

This is exactly where our Data Engineering Practice comes in, supporting companies that want to improve the reliability of their data and accelerate their projects.

Our support

Our experts work closely with your teams to understand your challenges before proposing solutions. We support you throughout the entire process: from auditing your existing data assets to deploying your data pipelines and ensuring long-term governance. We draw on our experience, programming skills, and expertise with data tools to ensure the success of every project.

What we solve

Is your data scattered across disparate systems that are difficult to consolidate?

We consolidate your data sources and build a data architecture tailored to your needs.

Are your pipelines fragile, manual, or not very scalable?

We manufacture them on an industrial scale and automate the process to ensure reliability and performance.

Is the quality of your data insufficient to support your reporting and AI projects?

We implement governance and quality control processes that enable you to turn it into a reliable asset for analytics, machine learning, and data team work.

Our technical expertise

Data architecture

(lakehouse, data warehouse, data mesh)

Ingestion & orchestration

(batch, streaming)

Processing & Modeling

(dbt, Spark)

Governance & Data Quality

DataOps & CI/CD data

Cloud data platforms

(AWS, GCP, Azure)

Our professions

Data Engineer

Data Architect

Analytics Engineer

DataOps

Examples of assignments

How we work

We always start by gaining a thorough understanding of your context before making any recommendations. There’s no one-size-fits-all solution: every data architecture is tailored to your specific requirements, your tech stack, and your product goals.

Our Data Engineers work closely with all of your Product, Design, Development, Data Analysis, and Data Science teams to ensure that data is never siloed but rather serves as a shared resource to support your products and business decisions. We foster a collaborative team dynamic that brings together engineers, scientists, and business professionals.

Do you have a project in Data Engineering?

APPROACH 1

Staffing

A consultant who works alongside your data teams to provide targeted expertise and support your technical and organizational goals. We strengthen your team with skills that are directly applicable to your data engineering projects. Whether you need a senior engineer or a specialized data scientist, our team adapts to the size and scope of your project.

APPROACH 2

Consulting & Auditing

Support from one or more experts on strategic or operational issues: an audit of your architecture, defining your data targets, and structuring your governance framework. We also analyze your tools, practices, and work processes. Drawing on our experience working with numerous companies, we help you streamline the various tools you use.

APPROACH 3

Customized workshop / training

A customized workshop lasting from half a day to three days to solve a data-related issue, define your target architecture, or educate your teams on best practices. We can incorporate targeted training to develop your teams’ skills. This tailored training helps align the internal skills essential for the deployment of your future projects.

They give us trust

BPCE
Radio France
France tv
Tarkett
SNCF Connect
Pathé
Engie
BNP Paribas
Samsung
Marionnaud
Groupama
Maisons du monde
Renault Digital
Schneider
Boursorama
Airbus
Infomil
Safran
Carrefour
Geev
Arkea
EDF
Betclic
CDiscount
Matmut
BPCE
Radio France
France tv
Tarkett
SNCF Connect
Pathé
Engie
BNP Paribas
Samsung
Marionnaud
Groupama
Maisons du monde
Renault Digital
Schneider
Boursorama
Airbus
Infomil
Geev
Safran
Carrefour
Arkea
EDF
Betclic
Cdiscount
Matmut

Data as the foundation of your performance

When it comes to data transformation, it all starts with the quality of the foundation. Without a robust architecture and reliable pipelines, no analytics project or AI model can deliver on its promises. Data is a strategic asset, but it must be reliable.

Data engineering is precisely this foundation: designing, building, and maintaining the infrastructure that enables data to be collected, transformed, and made available to those who need it, at the right time and in the right format. We incorporate the analytical, programming, tooling, and quality requirements expected by businesses.

An approach focused on your business challenges

Data is only valuable if it informs concrete decisions. Our experts don’t just build data pipelines; they understand your business challenges so they can design architectures that truly address them.

Understanding your technical and organizational contexts

A methodical and iterative approach

Close collaboration with your data, product, and business teams

Ongoing monitoring of data technologies and practices

Goal: to deliver reliable, scalable, and sustainable data infrastructure. Every architectural decision is tailored to your current needs and future goals. Because a strong data foundation is one that grows with you.

Consolidate your sources and improve the reliability of your data streams

Data scattered across heterogeneous systems means energy is wasted on reconciling information rather than deriving value from it. We work across the entire data ingestion chain—from collection to transformation—to build robust, automated, and well-documented pipelines.

This technical rigor is essential to ensuring that your analytics teams, data scientists, and business units can work with trustworthy data, using reliable tools for their analyses, projects, and machine learning applications.

Governance as a a driver of performance

A data architecture without governance is a fragile one. We incorporate data quality, traceability, and security considerations from the very beginning of the design process, ensuring that your data assets remain reliable and usable over time.

Data catalogs, access management, quality control, and documentation: these are all practices we implement to help your organization achieve sustainable data maturity.

We also establish work guidelines to facilitate collaboration between teams.

The Data for Product and Business Units

Our data engineers work closely with all relevant stakeholders—including data analysts, data scientists, product managers, developers, and business teams—to make data a common language that supports a shared vision.

This cross-functional approach speeds up projects, reduces friction between teams, and ensures that the foundations laid meet the organization’s actual needs.

The result: more autonomous teams, more informed decisions, and higher-performing products.

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