I built a data lake and reporting layer for a fintech company that needed better visibility across its product, operational, and business data.
The work meant consolidating fragmented sources, defining the visualization layer, building dashboards and reports, and working with developers to make the right data available in a usable format. The company and specific metrics are confidential.
The company had useful data spread across different systems, but it wasn't easily accessible for product, operations, or management reporting.
As the business grew, teams needed faster answers to basic questions: what was happening, where bottlenecks were emerging, how product flows were performing, and what needed attention.
Reporting depended too much on fragmented sources, manual work, and ad-hoc requests.
That made it hard to monitor performance consistently, understand operational issues, and make product decisions from a shared view of the data.
- ▹Designed and built the data lake structure
- ▹Created the data visualization and reporting layer
- ▹Built dashboards for product, operations, and management use cases
- ▹Defined key metrics and reporting logic
- ▹Worked with developers to expose missing data and improve availability
- ▹Translated business questions into data requirements
- ▹Created repeatable reporting workflows instead of one-off manual analysis
The aim was to make data usable by the people making the decisions: practical, understandable, and tied to real workflows like monitoring operations, finding bottlenecks, tracking product performance, and supporting planning.
I worked closely with engineering so the data model reflected how the product actually worked, instead of building dashboards on top of incomplete or poorly structured data and inheriting the gaps.
The system combined a centralized data lake with a visualization layer and dashboards built around the company's core operational and product metrics. The work required defining data sources, aligning metric definitions, identifying missing events or fields, and creating reporting views that could be reused across teams.
The project gave the company a more reliable way to understand product and operational performance. It reduced dependence on manual reporting, improved visibility across teams, and created a shared data layer for dashboards, reporting, forecasting, and product decisions.
Exact company details and metrics are confidential.