Robust Data Platform for a Digital Bank


Building a scalable customer-facing data platform with security at the core.

Railsr (previously Railsbank) is the pioneer in the global Banking as a Service (BaaS) sector, enabling banks, businesses and brands to define the future of consumer and SME finance experience.

Leading the way in making financial products and services digital and embeddable, Railsr enables deeper and richer customer experiences with the brands people love. Currently, the company is operating in the UK, Europe, SE Asia and the US, being the only global BaaS player.

What we did

Customer-facing analytics product
Cutting-edge data distribution platform
Standardised data integrations
Mentoring to ensure continuity

The Challenge

Railsr had a data platform at its infancy and needed some help to continue improving it with state of the art technologies and to deliver value to the business faster. They wanted to build a customer-facing platform to allow clients to check their cards’ usage data and prevent fraud.

The solution

Scalable customer-facing data platform

Removing concerns around vulnerabilities was one of the client’s main requirements as a BaaS solution. We started by modernising the existing data platform’s tech stack and optimising the architecture. This way, we improved the robustness of the platform and also solved any security challenges.

Business intelligence

On the business intelligence side, we built data models and implemented a reverse ETL solution to provide access to the required data on Salesforce. Furthermore, we created a customer-facing analytics product with 9 dashboards to allow Railsr’s customers to access their own data securely.

Data distribution

We teamed up with Railsr’s in-house engineers to design and implement a cutting-edge data distribution platform, delivering the design of the overall architecture. The customer-side components we built included a custom Spark Streaming service which persisted streaming data from Kafka onto Snowflake as well as dbt models to make such data available to internal users in semi-structured form.

Custom ingestion

Reusable internal libraries were created to improve stability, encourage code reuse and ensure compliance to internal standards. We abstracted Airflow DAGs creation to implement common policies, versioning , observability and testability, while reducing code by 82%. Finally, we implemented a custom Python ingestion library to standardise and simplify data integrations across Databricks and plain-Python contexts.


Over the entire length of the project, we would place a special focus on consulting, mentoring the client’s in-house data team and helping them adopt best practices. Full transparency on our side would allow them to become independent in managing their own infrastructure after project completion.

The technology we useD

The Result

Radical optimisation and uncompromising reliability

billion rows on Snowflake
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automated scheduled sync jobs
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0 %

- Contributed to the construction of a robust data platform for Railsr that prevents any data quality problems and offers minimal downtime;
- Implemented cost optimisation measures leveraging the scalability of the Data Cloud;
- Improved the development velocity for API data ingestions (1 week to 0.5 day).

Looker became the single source of truth for every department, including customer support, finance & risk, people & operations, product & engineering.

We launched a customer-facing data insights product as well that gives access to a series of aggregated data dashboards. Customers can now access a 30 day card overview, end-user behaviour, end-user spending, debit card engagement summary, ledgers, transactions, merchants and more.

Now Railsr has 29 sources of data integrated across 5 business units, over 70 active Looker dashboards, 230 active users, each user engaging with the data 4 hours a week on average.

Infinite Lambda have made tremendous and fundamental contributions to our codebase and helped set up an efficient modern data stack leveraging Snowflake, Databricks, Kafka, dbt and Gitlab.

Their talented engineers were able to seamlessly embed within our teams and deliver sustainable improvements to the platform with a refreshing, relaxed yet focused and disciplined approach.

All in all, I would recommend Infinite Lambda to any company needing green or brownfield implementation of a modern data stack.

Samy Doreau, Data Engineering Manager, Railsr

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