We support our clients on their data pipelines, their data warehouse and the workflows that feed them. What comes out is structured, documented data, ready to be activated: dashboards, media steering, AI use cases.
We work across the whole chain or on a single link: data architecture, tracking, ingestion, modelling, activation. You choose your entry point, depending on what already runs at your end and on your goals.
EdgeAngel is a data marketing and AI agency, and an independent consultancy. We have worked on marketing and analytics data since 2018, on scoped engagements or embedded alongside your teams.
Some clients call us to build from scratch, others have a warehouse that runs and figures they no longer trust, others want to activate what they already have. We come in at the link that is missing.
01Architecture and data platform
02Collection and ingestion
03Modelling and control
04Activation and use cases
01
Architecture and data platform
An architecture choice commits you for several years. It is decided on your use cases, your teams and your cloud.
Audit of what exists: sources, real uses, costs
Target architecture: data warehouse, data lakehouse, or both
BigQuery, Snowflake or Databricks, depending on your cloud
Everything is built inside your own cloud project
Access governance, GDPR compliance, hosting in Europe
Storage and query costs scoped from the design stage
Your data arrives from everywhere, at different rhythms, with schemas that change. It is the least visible link in the chain, and the one that needs the most monitoring.
We operate an ingestion platform for our clients, API-driven and scripted: a new scope is deployed and documented in a few days. We are an Airbyte Partner, and we also work on the ETL tools already in place at your end.
Upstream tracking: web, app and server-side collection
Ad platforms, CRM, analytics, back office, product: everything comes in
Off-the-shelf ETL and ELT, or a connector written for your source
This is where raw data becomes a figure someone can quote in a meeting. Your business rules get written, versioned and reviewed.
dbt or Dataform models, depending on your stack
Data marts per use: reporting, audiences, LTV, attribution
Business rules written down: definitions, exceptions, thresholds
Every field documented, readable by a human and by an agent
Consistency tests, data lineage, version history
A published accuracy control: the exposed table says the same thing as production
The transformation code lives in a versioned repository, with its history. You can read it, take it over, or hand it to someone else.
04
Activation and use cases
Modelled data goes back to your tools, your campaigns and your agents. That is where it produces value.
A CDP is a means. Depending on your context, we integrate it into the chain, we feed it from the warehouse, or we cover its function with the warehouse and reverse ETL. The choice is discussed at the framing stage.
Reverse ETL to CRM, ad platforms, business tools
First-party audiences and enriched conversions to the platforms
CDP: integrate it, feed it, or cover its role with the warehouse
Tables exposed to AI agents, with their documentation
The data architecture of one of our clients, as it runs today. It lets their marketing teams steer campaigns on real margin, and use AI on data whose accuracy is controlled. Here is the chain, link by link.
Each block is replaceable on its own. The choice depends on your cloud, your teams and your use cases, and it gets revisited.
01
Centralise
The back office loads into BigQuery every day, next to the ad platform and analytics data. A ride, the amount billed, its real cost, its completion status, its date. Media cost data arrives through the same pipelines, at the same rhythm.
Sources arrive exactly as they are. Normalisation happens in the warehouse, where it can be read.
02
Reconcile and model
A ride billed in the back office and a click paid for in an ad platform are two events with nothing in common. We connect them through the transaction identifier, then we recover the click identifier and the consent state of each ride from Google Analytics. The real margin is then computed in the warehouse, with the client rules, in a definition that is written down and versioned. Missing data stays empty.
The join that connects money collected to the click paid for, and the calculation rule exactly as it is written.
03
Activate
The modelled margin serves two uses at once. A dashboard becomes the single source of truth for budget steering, the one the client and the consultant look at to decide. And the value goes back to the ad platform as an offline conversion import, through the Data Manager API, so that the bidding strategy targets margin.
The same figure serves human decisions and automated bidding. That is what keeps the two consistent.
04
Expose and analyse
The modelled tables are documented and opened to the AI platforms of the consultant and the client. Finer analysis happens there: incrementality, seasonality effects, breaking down a margin gap. Every field carries its definition, so an agent answer traces back to its source.
Documenting the fields is what makes a table queryable by an agent, and the control is what makes its figures verifiable.
Our engagement formats
Two ways of working with us
We offer both, and they often combine. What changes is the format of the engagement and who carries the design work.
Embedded consultantsScoped engagement
Who it is for
Embedded consultants You have a data team and a roadmap, and you are missing one skill or extra hands
Scoped engagement You want a scope delivered, documented and taken over by your teams
Format
Embedded consultants A consultant embedded in your team and your rituals, by the day or on a fixed price
Scoped engagement An engagement with a scope, a schedule and named deliverables
Rhythm
Embedded consultants You steer, at the pace of your sprints
Scoped engagement First data in production within weeks, then extension
Design work
Embedded consultants Carried by your teams, we execute and advise
Scoped engagement Carried by us, validated with you
At the end
Embedded consultants The skill has moved into your team
Scoped engagement The code, the models and the documentation are yours
We cover the whole chain: architecture, ingestion, orchestration, modelling, governance, activation. We are particularly useful on defining and shipping marketing use cases, because we know the tools that consume the data and the actions that produce a measurable result.
We work autonomously on the scope you entrust to us, or alongside your IT department, your data teams and your integrators. How the work is split is decided at kick-off, and adjusted as the project runs.
Embedded
The data profiles we place
A consultant embedded in your team and your rituals, for a defined period. We draw on our internal profiles and on our network of partners, agencies and independents.
Data EngineerBuilds and operates the ingestion pipelines and the warehouse.
Analytics EngineerModels and documents the data your tools and your agents read.
Data ArchitectDesigns the target architecture and arbitrates technical choices.
DataOps EngineerIndustrialises deployment, orchestration and monitoring.
Data AnalystBuilds the dashboards and the analysis on the delivered model.
Data marketing consultantConnects the business need with the data that is available.
Our method
How we work
We start from a use case with clear value, we build the chain it needs, and we extend from there. One accurate figure in production beats a complete architecture on paper.
Essential for driving and optimizing marketing, maximizing performance, and limiting regulatory risks.
02.
Premium Consultants
Responsive, proactive with solid and proven expertise in AI & digital marketing data.
03.
Personalized Collaboration
EdgeAngel adapts to your organization for a tailor-made and efficient collaboration.
Testimonials
What our clients say.
Partners since 2018 on our tracking and privacy challenges (Piano, sGTM, GDPR), EdgeAngel combines sharp technical expertise with rigor. An essential extension of our team to secure and accelerate our complex data projects.
Since 2021, EdgeAngel has been securing our Web & App tracking. Their advanced technical expertise has enabled us to regain full control of our data to effectively drive the evolution of our digital ecosystems.
François Charlet Communication and Digital Marketing Citeo
With the Capture solution for our reporting and the quality of consultants, our performance reviews have become ultra-clear and relevant. This allows us to manage daily activities and gives us the means to achieve our goals.
Mathias Pestre-Mazieres CEO Blue Horse Group
An acquisition strategy that perfectly secured our high season. Thanks to fine-tuned management of the Google / Meta complementarity, we were able to scale our volumes with controlled profitability.
Lydie Castagnet Marketing & Communication Director Forge Adour
EdgeAngel is not limited to media buying, they give meaning to numbers. They were able to detect weak signals in our data to transform our acquisition and unlock new performance levers.
Valentine Soulès E-commerce Manager Golf One 64
Since the deployment of Capture by EdgeAngel, our SEO team has significantly improved reporting quality and opportunity analysis: the reports are highly performant, with very advanced granularity (+30 million rows in the dataset) and complete team adoption.
Karim Elmlih Head of Organic Acquisition – Growth Qonto
Our solution
Capture: the modular Data Platform to activate your Marketing & AI data.
Connect your tools to each other and feed your AI and agentic workflows with your data and your business context. Capture augments your existing infrastructure and makes it more efficient: you remain the owner of the tools and the data.
Frequently asked questions about the Modern Data Stack
What is a Modern Data Stack?
A Modern Data Stack is a set of specialised building blocks that collect, centralise, transform and activate a company data, connected by automated pipelines. Each block is replaceable independently of the others.
In practice: ingestion brings the data in, the warehouse stores it, transformation shapes it according to your business rules, activation sends it back to your tools. Governance and documentation run across all four.
Which tools make up a Modern Data Stack?
A Modern Data Stack is organised in six categories:
Ingestion and synchronisation: Airbyte, Fivetran, Stitch
Storage and warehouse: Google BigQuery, Snowflake, Databricks
Transformation: dbt, Dataform
Activation and reverse ETL: Hightouch, Census, Segment
Visualisation and BI: Data Studio, Power BI, Metabase
Governance: data catalogue, data lineage, access control, GDPR compliance
The choice depends on your maturity, your teams, your cloud and the use cases you target.
On sovereignty, a European foundation is a real decision criterion: OVHcloud and Scaleway on the cloud side, Didomi on consent management. It is addressed at the framing stage, with your regulatory constraints.
What is the difference between a Modern Data Stack and a data platform?
The Modern Data Stack refers to the modular blocks that collect, transform and activate data. The data platform refers to the technical foundation they sit on: cloud infrastructure, storage, orchestration, security, access management.
The two are designed together. A stack built on a poorly scoped foundation is expensive to operate and hard to evolve, and a foundation with no uses is just a cloud bill.
BigQuery, Snowflake or Databricks: how do you choose?
The choice is made on your cloud, your internal skills and your cost profile. If you are already on Google Cloud, BigQuery is the short path: per-query billing, no cluster to size, native integration with the advertising and analytics ecosystem. On AWS or Azure, or with teams already trained, Snowflake and Databricks have their arguments, particularly on heavy compute workloads and machine learning.
Our deepest expertise is on BigQuery, and we say so. If your foundation is elsewhere, we work on it: designing the model weighs more than the engine that runs it.
dbt or Dataform for transformation?
Both do the same job: writing SQL transformations that are versioned, tested and documented. dbt is the market standard, with the largest ecosystem and community, often the right choice if your teams already know it or if your warehouse is outside Google Cloud. Dataform is native to BigQuery, with no extra licence and no separate orchestrator, and it lives inside your Google Cloud project.
We work with either. What matters more: that models are versioned, that business rules are written down and that tests exist.
Does a CDP replace a Modern Data Stack?
No, they answer two different needs. A CDP unifies customer profiles and activates them towards marketing tools, with an interface built for business teams. A warehouse stores all of your data, including what has nothing to do with the customer, and serves analysis as much as activation.
Three situations come up in practice. You have a CDP: we integrate it into the chain and feed it from the warehouse, which stops it becoming a second source of truth. You are evaluating one: we scope what it adds against a warehouse plus reverse ETL. You would rather do without: the function is covered by the warehouse, the modelling and the activation.
How do you know the figures coming out of the stack are accurate?
Through a written control. We publish a reconciliation between the table exposed to the tools and the production view that serves as reference: the same aggregates, over the same period, row by row, with the gap shown. The control is replayed on every change to the model.
Alongside that, consistency tests run on the models, freshness and completeness checks run on ingestion, and data lineage says where each field comes from. Missing data stays empty and gets flagged.
Who owns the code and the data in the stack?
You do. And it is a question worth asking any provider, because the answer varies.
With us: the warehouse and the modelled tables live in your own cloud project, the transformation code is versioned in a repository with its history, and the model documentation is delivered. If you take over or change partner, you leave with all of it.
One honest nuance: depending on the architecture chosen, part of the ingestion flow may transit through a platform we operate. That is a choice discussed at the framing stage, and it is documented.
How long does a first chain in production take?
A few weeks for a first scope delivered and documented: two or three sources, a warehouse, a model and one use served. A few months for a complete chain across all sources.
The factor that weighs most is access. Getting the rights on an ad platform, a CRM or a back office often takes longer than connecting the flow, and it is prepared from the framing stage.
Can you take over an existing stack?
Yes, and it is the starting point of many of our projects. A stack taken over comes with its reasons: a provider gone, a team that changed, a project stopped halfway.
We start with a review: what runs, what stopped running without anyone noticing, what is documented, what it costs. Then we decide with you what is kept, what is rebuilt and what is switched off. We also know how to hand a stack we built over to your teams or to another partner.