Decision-first hierarchy
Primary business questions lead the interface; supporting channel charts follow rather than compete for attention.
Dashient Reporting Dashboard
Overview
A client-facing reporting product that brings spend, behaviour and enquiries into one filterable view.
- Role
- Product design · Full-stack engineering · Data visualisation
- Stack
- Flask · Python · SQL · Postgres · Recharts · SaaS
01 / Challenge
The problem worth solving.
Monthly PDF reports were static and easy to ignore. Clients needed an on-demand view of performance without learning several analytics platforms.
02 / System architecture
How the parts connect.
- 01
Scheduled source pipelines
- 02
Warehouse models
- 03
Flask API
- 04
Account-scoped access
- 05
React and Recharts interface
03 / Implementation
From ambiguity to a working system.
- 01
Designed and shipped the scheduled pipelines, warehouse models, Flask API and client-facing interface end to end.
- 02
Scoped access by client so each account resolves only its own reporting rows.
- 03
Computed date-range and channel filters server-side to keep the interface responsive and definitions consistent.
- 04
Designed the visual hierarchy around decisions: enquiries, spend, cost per enquiry and source-level comparison.
04 / Engineering decisions
Server-owned calculations
Shared calculations prevent different screens from drifting into conflicting definitions.
Account isolation
Per-client access control is part of the data path rather than an interface-only filter.
05 / Outcomes and current status
End-to-endowned solo
Role-basedclient access
Fixed-pricemonthly plans
06 / Constraints and limitations
What the demonstration does not claim.
- The portfolio preview uses illustrative data and is not a production client account.
- The public view omits client identities, credentials and commercially sensitive records.
- A live product must remain useful when one upstream source is delayed or unavailable.
07 / Technology
FlaskPythonSQLPostgresRechartsSaaS