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
As part of the management team of an e-commerce site for electronics and household appliances, a consultant was called in to improve the purchasing and post-purchasing customer experience. Analysis of sales KPIs, NPS, customer service activities and user feedback to define a strategy and roadmap, and proposal of a Discovery methodology.
Working for a customer in the supply chain sector, our team, comprising two Product Managers and a Product Designer, was dedicated to the delivery and discovery of their applications. Our intervention was also aimed at guiding the customer towards a more product-centric approach (frequent delivery, OKR, product vision, design process, post-implementation follow-up).
As part of the Product teams of a player in the sports betting industry, our Product Manager leads a multi-profile team: Tech, QA, Design. He is involved in identifying and prioritizing Product initiatives in the context of the creation of a new Offers platform. He leads the Delivery activity in an environment with strong dependencies between Product squads.
In an agritech startup developing autonomous robots, our Product Manager structured the Product organization to support its ramp-up. The mission began with a co-constructed 3-5-year product vision, which was then translated with the teams. A diagnosis of 20 key themes enabled us to measure product maturity and prioritize 33 concrete recommendations. The intervention laid down the fundamentals: clarification of roles, adoption of OKRs, Definition of Done, framing of tests, structuring of rituals. The result: faster decisions, better coordination, smoother development cycles and enhanced performance.
As part of the digital department of France's leading radio group, our Product Management consultant oversaw the design, implementation and roll-out of a new program schedule management tool for all the group's channels. The mission began with an in-depth Discovery phase (user interviews, benchmarking of other media practices, "make or buy" arbitration), and continued with the definition of a clear Product vision, the elaboration of a structured roadmap, the delivery of an operational MVP, and then the progressive roll-out to each channel.
Within the digital department of France's leading radio group, a consultant led the design, implementation, and rollout of a new program schedule management tool for all of the group's channels. The project began with an in-depth discovery phase (user interviews, benchmarking of other media practices, "make or buy" decision-making), followed by the definition of a clear product vision, the development of a structured roadmap, the delivery of an operational MVP, and then gradual deployment across each channel.
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
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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