With a team of +30 data specialists and software engineers, it is the biggest team at Echo Analytics!
Our delivery approach blends:
- Lean management principles, with ‘Gemba walks’ to reflect on process improvements, ‘problem-solving’ sessions within product teams, and ‘Tech Obeya’ to centralize all key dashboards (product roadmap, POI Datalake, delivery performance), and highlight the improvements we can implement.
- Agile methodologies with two-week sprints, with our Product Managers overseeing the roadmap.
The Echo Tech team plays a pivotal role in delivering Echo's products, by building and maintaining large-scale data processing and machine learning pipelines.
Team Organization
We are divided into 4 data-centric product teams, with multidisciplinary skills (Data Engineering, Data Analysis, and machine learning).
Here are their scope:
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Pinpoint on maps millions of “Point-of-interest” data on worldwide commercial locations.
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A dataset that provides all attributes to understand activity and trends in and around commercial places without personal data (Footfall, cross visitation, catchment area…).
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A team working on the “Geopersona” product, a map-based audience segmentation (brands, interest, purchasing power…) based on mobility patterns and customer behaviors.
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Based in Bangalore (following Roam acquisition). Engineers working on mobile programs to track in real-time users’ locations and be able to store and display them.
Tech Stack
2025 Challenges & Roadmap
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Echo Platform
Developing an internal SaaS platform that is the cornerstone of all our products. We already integrated Mobility (Geopersona) within the platform (+80M POI), before adding "Places" (+80M POI) and "Activity" by year-end.
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Delivery on scale
Using LLM and prompting to improve how we answer our customer requests. We plan to launch the first version of an AI delivery copilot to take customer requests and run relevant SQL queries. Then we will implement it for our Sales team before integrating Prompter into the Echo Platform.
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Foundation model
Enriching our product lines with a Large Geospatial Model (LGM) for forecasting and simulation use cases. Before training large-scale geolocalization models, we plan to build our foundations on quality datasets and small-scale multi-modal models (POIs, Brands, …).