Modern Data Stack and Data Engineering Agency

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.

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Airbyte Partner Airbyte Partner

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.

Data architecture: sources (Google Ads, Meta, GA4, CRM, back office), the client warehouse with its raw, staging and marts datasets, then the uses
Architecture and data platform We design the architecture with you, and we build it inside your own cloud project.
Active connections console: each source with its connector (Airbyte, Fivetran, Segment, Capture) and its loading schedule
Collection and ingestion We connect your ad platforms, your CRM, your analytics and your back office, and we monitor what comes in.
The three layers of the data model, from raw to business tables, and the real margin definition written out with its rule on missing data
Modelling and control We write your business rules into the warehouse, versioned and readable by your teams.
The four destinations of modelled data: steering dashboards, ad platforms, business tools through reverse ETL, AI agents
Activation and use cases We send modelled data back to your dashboards, your ad platforms and your agents.
Two EdgeAngel consultants at work in front of a screen, in the agency offices
Embedded consultants We embed a consultant in your team and your rituals when that is the format that suits you.

They trust us

Crédit Agricole
Cerland
Guerlain
Beemoov
Beauty Success
Citeo
ETS
Smartphone iD
La Ferme du Mohair
Forge Adour
Nomadeshop
La marque en moins
IZI by EDF
Ponant
Kiloutou
Aimigo
Atelier Particulier
Cotesushi
Soulet
Randonades
M&A
Cheval Energy
HappyWool
Rexel
Qonto
Belambra
Gamarde
Devensys
Eduservices
PonyPower
Golf One
WeDressFair
LegalPlace
Finary
Le Collectionist
Bostik
Citygo
Allocab
Mercedes-Benz
Bip&Go
Hoff
Orange Bank
Upcoop
Legallais
Louis Vuitton
Interencheres
Crédit Agricole
Cerland
Guerlain
Beemoov
Beauty Success
Citeo
ETS
Smartphone iD
La Ferme du Mohair
Forge Adour
Nomadeshop
La marque en moins
IZI by EDF
Ponant
Kiloutou
Aimigo
Atelier Particulier
Cotesushi
Soulet
Randonades
M&A
Cheval Energy
HappyWool
Rexel
Qonto
Belambra
Gamarde
Devensys
Eduservices
PonyPower
Golf One
WeDressFair
LegalPlace
Finary
Le Collectionist
Bostik
Citygo
Allocab
Mercedes-Benz
Bip&Go
Hoff
Orange Bank
Upcoop
Legallais
Louis Vuitton
Interencheres
Allocab
Randonades
Cheval Energy
Atelier Particulier
Orange Bank
Gamarde
Bip&Go
Upcoop
LegalPlace
Golf One
PonyPower
Soulet
ETS
Kiloutou
Citeo
HappyWool
Aimigo
Crédit Agricole
Beemoov
Forge Adour
Belambra
M&A
Finary
Nomadeshop
Devensys
Cotesushi
WeDressFair
Le Collectionist
Mercedes-Benz
Beauty Success
Cerland
Smartphone iD
Hoff
La marque en moins
IZI by EDF
Eduservices
Guerlain
Louis Vuitton
Interencheres
Qonto
Rexel
Citygo
Bostik
La Ferme du Mohair
Legallais
Ponant
Allocab
Randonades
Cheval Energy
Atelier Particulier
Orange Bank
Gamarde
Bip&Go
Upcoop
LegalPlace
Golf One
PonyPower
Soulet
ETS
Kiloutou
Citeo
HappyWool
Aimigo
Crédit Agricole
Beemoov
Forge Adour
Belambra
M&A
Finary
Nomadeshop
Devensys
Cotesushi
WeDressFair
Le Collectionist
Mercedes-Benz
Beauty Success
Cerland
Smartphone iD
Hoff
La marque en moins
IZI by EDF
Eduservices
Guerlain
Louis Vuitton
Interencheres
Qonto
Rexel
Citygo
Bostik
La Ferme du Mohair
Legallais
Ponant
La Ferme du Mohair
Louis Vuitton
Beemoov
La marque en moins
Le Collectionist
Belambra
M&A
Rexel
Soulet
Ponant
Atelier Particulier
Interencheres
Legallais
Nomadeshop
Gamarde
PonyPower
Cotesushi
IZI by EDF
Qonto
Devensys
Golf One
Guerlain
Finary
Eduservices
Cerland
Cheval Energy
Citeo
Aimigo
Kiloutou
Orange Bank
WeDressFair
Bostik
Randonades
Bip&Go
Forge Adour
HappyWool
Allocab
Citygo
ETS
LegalPlace
Upcoop
Mercedes-Benz
Beauty Success
Crédit Agricole
Hoff
Smartphone iD
La Ferme du Mohair
Louis Vuitton
Beemoov
La marque en moins
Le Collectionist
Belambra
M&A
Rexel
Soulet
Ponant
Atelier Particulier
Interencheres
Legallais
Nomadeshop
Gamarde
PonyPower
Cotesushi
IZI by EDF
Qonto
Devensys
Golf One
Guerlain
Finary
Eduservices
Cerland
Cheval Energy
Citeo
Aimigo
Kiloutou
Orange Bank
WeDressFair
Bostik
Randonades
Bip&Go
Forge Adour
HappyWool
Allocab
Citygo
ETS
LegalPlace
Upcoop
Mercedes-Benz
Beauty Success
Crédit Agricole
Hoff
Smartphone iD

Our approach

The four links of a data chain

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.

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
02

Collection and ingestion

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
  • Orchestration, retries on failure, history kept
  • Freshness and completeness checks on what arrives
  • One single place to know whether a flow ran
03

Modelling and control

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
  • Dashboards and budget steering on a single source

Use case

Steering the business on margin

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.

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 Engineer Builds and operates the ingestion pipelines and the warehouse.
  • Analytics Engineer Models and documents the data your tools and your agents read.
  • Data Architect Designs the target architecture and arbitrates technical choices.
  • DataOps Engineer Industrialises deployment, orchestration and monitoring.
  • Data Analyst Builds the dashboards and the analysis on the delivered model.
  • Data marketing consultant Connects 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.

More about the agency
  1. 01

    Audit

    Sources, volumes, quality, costs, and above all the real uses. We look at what your teams do with data today, and what they would like to do with it.

  2. 02

    Design

    Target architecture, tool choices, data model, access governance. A document your teams can challenge.

  3. 03

    Build

    Ingestion, models, activation. One scope in production, with its documentation and its controls, before moving to the next.

  4. 04

    Keep it alive

    Monitoring, schema changes, new uses, skills transfer to your teams. A data chain keeps moving with your business.

We work on an audit alone, on a full build, on taking over an existing stack, or by placing a consultant with you.

The EdgeAngel Difference

01.

AI, Data & Marketing
Expertise

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.
    Kiloutou Cédric Tamboise

    Cédric Tamboise Group Digital & e-Commerce Director Kiloutou

  • 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.
    Citeo François Charlet

    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.
    Blue Horse Group Mathias Pestre-Mazieres

    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.
    Forge Adour Lydie Castagnet

    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.
    Golf One 64 Valentine Soulès

    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.
    Qonto Karim Elmlih

    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.

Discover Capture

See also Cookie Consent

The agency

An independent firm of senior consultants, founded in 2018.

Hubs in Paris, Bordeaux and Barcelona. You know who works on your account, and you talk directly to the consultants who run it.

2018
founded
3
hubs: Paris, Bordeaux, Barcelona
8
official partnerships

FAQ

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.

Get in touch

You leave your details, we get back to you within 24 hours.

A first call to understand your context, and tell you what is feasible and under which conditions.

  1. Contact details
  2. Defining your need
  3. Then we schedule a call

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Or call us directly: +33 1 84 16 42 20

Paul Schmitt

Paul Schmitt

Consulting Director

"Our goal is to make your data actionable to generate concrete value, quickly."

The team