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 solutions: the right tools, the right skills, and a solid approach to data engineering.
The finding
Without a solid data foundation, all your analytics and AI projects are built on sand. Poor data quality, fragile pipelines, unqualified data engineers or data scientists, and silos between systems—these issues hinder the entire management and organizational process, thereby delaying 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 data engineers 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 pipelines and ensuring long-term governance. We draw on our experience, our management and programming skills, and our 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, learning, and quality control processes that enable you to turn it into a reliable asset for analytics, machine learning, and the work of data teams.
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 begin by gaining a thorough understanding of your context before making any recommendations. There’s no one-size-fits-all solution: each data architecture is tailored to your management processes, constraints, tech stack, and 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 a shared resource that supports your products and business decisions. We bring every engineer, scientist, and business professional together as part of a unified team to develop solutions tailored to your needs.
Do you have a project in Data Engineering?
APPROACH 1
Staffing
An engineering consultant who integrates into 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 a strategic or operational issue: an audit of your architecture, defining your target data, and structuring your governance. We also analyze your tools, practices, and work organization. Drawing on our experience working with numerous companies, we help you explore solutions to streamline your various off-the-shelf tools.
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 wasted management effort spent 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 ensure 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 architecture. We incorporate data quality, traceability, and security considerations from the very beginning of the design process to ensure 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.
FAQ - Data Engineering
A data warehouse stores structured and modeled data for analysis, with a schema defined in advance. A data lake stores raw data in any format, without prior modeling. The lakehouse combines the best of both worlds: the storage flexibility of the lake with the transactional guarantees and analytical performance of the warehouse.
Data mesh is a decentralized approach to data management: each business domain becomes responsible for its own data, which is exposed as documented and governed data products, rather than being centralized within a single team. It addresses an organizational scaling challenge and requires a certain level of data maturity to be already in place.
ETL transforms data before loading it, while ELT loads it in its raw form and then transforms it in the data warehouse. ELT has become the standard with cloud platforms, whose computing power makes transformation at the target more cost-effective and flexible. ETL remains relevant when volume, cost, or privacy constraints require filtering to be performed upstream.
By implementing automated checks on explicit metrics: completeness, uniqueness, recency, referential consistency, and compliance with business rules. These checks run within the pipelines and trigger alerts, rather than being discovered by a user looking at an inconsistent dashboard. Without a designated owner for each dataset, no system can be sustained over time.
Not necessarily. The cloud provides elasticity and managed tools that speed up the deployment of pipelines, but it does not fix poorly defined data or a lack of governance. A migration carried out without first addressing data modeling and quality will simply replicate the same problems at a higher operational cost.
DataOps applies DevOps practices to data pipelines: versioning of transformation code, automated data testing, continuous integration and deployment, and observability of processing. The goal is to make changes to the data pipeline as reliable and traceable as those made to an application.
The data engineer builds and maintains the infrastructure that makes data available: ingestion, pipelines, modeling, and quality control. The data scientist then uses this data to produce advanced analyses or machine learning models. The two roles follow one another in the value chain rather than overlapping: without reliable upstream processing, a model cannot deliver on its promises in production. In practice, the boundary between the two roles shifts depending on the organization’s maturity. In a small data team, a single person often covers both areas until the volume of data or the number of use cases makes it necessary to specialize the roles.
Yes, and that is the most common cause of failure. A machine learning model trained on incomplete, poorly documented, or outdated data will produce unstable results in production, regardless of the algorithm’s quality. This doesn’t mean you should wait for a perfect architecture: it’s better to prioritize ensuring the reliability of the datasets and processing methods that the use case actually relies on. In practice, the artificial intelligence projects that stand the test of time are those in which data engineers and data scientists work together to define data requirements from the very start of the first POC.
The issue is less about the volume of data than about the number of sources that need to be reconciled and the critical nature of downstream uses. An organization that generates reports from two systems can operate with shared responsibility. As soon as the number of sources increases, when data processing must run automatically and reliably, or when a pipeline error affects a business decision, the role becomes a structural one. The clearest indicator remains the time spent: when your analysts devote more energy to reconciling data than to analyzing it, the role of data engineer warrants a full-time position.
Batch processing handles data in batches at regular intervals, while streaming processing handles data as it arrives in real time. The deciding factor is not modernity but the actual timeliness required by the use case: a report viewed every morning gains nothing from being updated continuously, whereas fraud detection or a machine learning recommendation requires it. Streaming involves higher operational and development costs and makes error recovery more complex. Most platforms combine both processing modes depending on the data streams, rather than relying on just one.
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