Portfolio brief · plain-text edition · updated 2026-09-02
Damián Hernández
AI Product Designer for Complex Systems
- Level
- Staff Product Designer at Atlassian
- Experience
- 16+ years designing enterprise and AI-native products
- Location
- San Francisco Bay Area · remote-first
- History
- Atlassian (2020–present), GitHub via the Gitalytics acquisition, Wizeline
- Looking for
- Staff / Principal product design roles at AI-native companies where design has real product influence.
- Contact
- thedamianhdez@gmail.com · LinkedIn · Book 15 min · Résumé (PDF)
Staff-level product designer with 16+ years building tools people trust. At Atlassian, I design AI usage and metering, explainable customer intelligence, design systems, and agentic workflows for enterprise-scale products. Accessibility (WCAG AA) is the floor, design systems are the mechanism, and I build agentic design tooling — Figma MCP, Claude Code, Replit Agent — to compress the distance between intent and shipped UI without losing human control.
Focus
- AI explainability and human control
- Usage-based pricing and metering UX
- Agentic workflows and design-to-code tooling
- Design systems and data visualization at platform scale
- Accessibility as a product quality bar
Case studies
The full site is at www.damianhdez.com. This page carries the same facts without the interface.
Unified sales intelligence — from zero to org-wide adoption
- Role
- Principal Designer
- When
- 2024 - 2026
- Domain
- Enterprise, Data Platform, Sales Intelligence, AI/ML
- Access
- Summary public; full study on request
An AI-powered sales intelligence surface that turned fragmented customer signals into a 2-minute pre-call brief with context, confidence, and next actions.
Before every 30-minute customer call, Atlassian sales reps spent 60 minutes across 8 different tools — copying account data, chasing renewal dates, scanning support tickets. The information existed. The context didn't.
Outcomes
- 35% Sales Efficiency (time saved · 2+ hrs/day per rep)
- 28% Deal Velocity (faster close rate)
- 100% Rep Adoption (in 90 days, zero mandate)
AI-powered customer intelligence — from reactive support to proactive success
- Role
- Principal Designer
- When
- 2024 - 2026
- Domain
- Customer Intelligence, AI/ML, Predictive Analytics, Platform
- Access
- Summary public; full study on request
A real-time customer intelligence layer designed to make predictive signals understandable, actionable, and available inside the tools teams already use.
Atlassian had rich customer data spread across Support, Sales, Product, Success, and Leadership — five teams, five tools, five versions of the truth. The result: a consistent 60-day delay between the first sign of customer distress and any meaningful intervention.
Outcomes
- 47d → 3.5d Time to Customer Value (93% reduction)
- 89% Churn Prediction Accuracy (vs industry avg ~60%)
- 34% Support Ticket Reduction (through early intervention)
- 78% Alert Action Rate (of alerts acted on)
The shared dashboard canvas powering visualizations across Atlassian's products
- Role
- Lead Designer
- When
- 2024
- Domain
- Platform Systems, Interaction Architecture, Visualization Infrastructure
- Access
- Summary public; full study on request
I helped turn dashboards from a standalone analytics feature into a shared Atlassian platform capability: one creation flow, one editable canvas, and a reusable contract for visualizations from any product or data source.
Atlassian had powerful visualization experiences, but they lived inside individual products. Creating a dashboard often meant starting from a specific product, inheriting its data model, and rebuilding familiar interactions in a new place.
From side project to Atlassian's future-facing chart language
- Role
- Lead Designer
- When
- 2022 - Present
- Domain
- Design Systems, Platform Design, Leadership, Scale
- Access
- Public
A self-initiated data visualization system that turned inconsistent charts across Atlassian into reusable, accessible design infrastructure for the next generation of product and AI experiences.
Nobody asked for this. That's the point.
Outcomes
- 120+ Teams Adopting (product teams)
- 400+ Community Members (in Slack for chart work)
- 2–3 weeks Design-to-Implementation (→ days)
Unifying Atlassian's data architecture — designing the experience layer
- Role
- Lead Designer
- When
- 2022 - 2023
- Domain
- Systems Design, Architecture, Privacy, Scale
- Access
- Summary public; full study on request
Atlassian had data everywhere — Jira, JSM, Confluence, Assets — but no way to see across it. The design challenge was making that infrastructure human.
Atlassian had data everywhere — Jira Software, JSM, Confluence, Assets — but no way to see across it. The Atlassian Data Lake was the infrastructure answer: a unified store that aggregates product data into a single queryable source. The design challenge was making that infrastructure human.
From acquisition to Enterprise Cloud driver
- Role
- Lead Designer
- When
- 2021 - 2024
- Domain
- Platform Design, Data Visualization, Enterprise, Acquisition
- Access
- Summary public; full study on request
From acquired data product to trusted Enterprise Cloud platform: I owned the core design surface and built foundations for accessible, explainable decision-making.
When Atlassian acquired its first data-visualization product, there was no blueprint. The acquired product needed to become a genuine Enterprise Cloud offering — not just rebranded, but redesigned from the inside for the scale, integration requirements, and enterprise trust signals that Atlassian's customers expected.
Outcomes
- 120+ Teams Adopting Platform (via component library)
- 0.3 → 6.3 Filter Ease Score (SEQ improvement)
- Acquisition Product Trajectory (→ Enterprise Cloud)
From zero-to-one startup to an analytics product inside GitHub
- Role
- Founding & Solo Designer
- When
- Startup → Acquisition → GitHub
- Domain
- Startup, 0-1 Product, Acquisition, GitHub
- Access
- Public
I built Gitalytics as its solo designer, then evolved the product into GitHub Insights after acquisition — scaling a scrappy analytics startup into a native part of the GitHub ecosystem.
Engineering managers were flying blind. Git repositories held a full record of team activity — commit patterns, pull-request velocity, review bottlenecks, and contributor distribution — but turning that raw data into a useful team signal required custom queries and constant interpretation.