Case study

Data Integration

DataBridge is a modern ELT data integration platform that empowered low-code users with a simple and intuitive way to create data pipelines and seamlessly integrate data on the cloud. With its guided and user-friendly product experience, users can effortlessly access and integrate new data into their analytics workflows. This ensures that they have access to the latest and most accurate data, enabling them to make timely and well-informed business decisions that positively impact their organization.
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DataBridge is one of our data integration platforms that offered our low-code users an easy way to create data pipelines and integrate data on the cloud. With its guided, seamless, and low-code product experience, users can effortlessly access and integrate new data for their business analysis. This ensures they have up-to-date information to make timely and informed decisions that will positively impact their business.


Leading the data integration platform UX team, building the product design vision, strategy and producing UX design mockups, conducting usability testing, and collaborating with stakeholders and communicating with the executive leadership team

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  • (User research recruiting platform)


Our no/low-code users faced challenges when connecting and transforming data, as well as creating data pipelines using existing solutions provided by competitors. Additionally, they encountered difficulties when trying to pull analytical insights securely and continuously.


We tackled the challenges of connecting and transforming data by providing our users with a comprehensive and user-friendly platform that simplified the process of creating data pipelines with no-code solutions. Our solution offered intuitive tools, automation features, templates, connectors that catered to general data related use cases. We prioritized ongoing innovation and updates to ensure that our platform stayed up-to-date with the latest advancements in data integration technology, providing the most efficient and effective solution possible. As a result, we were able to deliver a seamless and user-friendly experience to our users, equipping them with all the necessary tools to meet their data integration needs.

UX Research

I conducted initial user research by talking to data managers and users to get insights. The following problem statements outline the key challenges and considerations involved in helping our users connect, transform, and analyze data efficiently and securely on our platform.

  1. What are the current challenges our users face when connecting and transforming data?
  2. In what ways are our users struggling to create data pipelines using no-code solutions?
  3. How can we ensure our users are able to securely and continuously pull analytical insights in a timely manner?
  4. What tools or features can we offer to simplify the process of connecting and transforming data for our users?
  5. How can we provide our users with a more intuitive and user-friendly experience for creating data pipelines with no-code?
  6. What security measures can we implement to protect our users' data while they're using our platform to continuously pull analytical insights?
  7. Are there any specific industries or use cases where our users may face unique challenges when it comes to connecting and transforming data? If so, how can we address these challenges?
  8. How can we ensure our platform stays up-to-date with the latest advancements in data integration technology, to provide our users with the most efficient and effective solution possible?

After defining key research questions, I planned and led to conduct 14 qualitative 14 user research sessions.

By establishing specific guidelines for data volume and data synchronization frequency, I empowered the engineering department to effectively plan for and ensure the scalability of our platform.

Leveraging the insights gathered from user research, I identified 10 key UX KPIs to guide the development of our product. Additionally, I extracted ten crucial user insights that informed our decision-making process.

To secure buy-in from both the executive leadership team and stakeholders in various departments, I effectively communicated ten key user insights that highlighted the value and importance of collaboration.

Our key personas were a data analyst, data engineer, line of business executive, data admin based on our research.


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I planed 11 research and design sprints to complete the end-to-end product design and helped the team to meet the high-quality user experience bar and the 10 key users values that previously defined.

We conducted user testing on 10 different design variants, and based on the feedback received, we selected the UX that was the most well-received by users.


During the development phase, the team used agile development methodologies to build the platform's features. We worked in sprints to ensure that regular feedback and progress updates were incorporated from stakeholders. In the testing phase, the team verified that the platform's features functioned as intended and met user requirements. We used both automated and manual testing methods to identify and address any bugs or issues. The deployment phase involved releasing the platform to users. The team ensured that the platform was deployed in a way that was secure, scalable, and reliable to ensure a positive user experience.