Data · Engineering · Product Design
Newsroom
Tools
I work inside live newsroom systems — adding new data pipelines and
templates, prototyping better workflows, and shipping lightweight features on top of
existing tools — so journalists can turn data into stories under real broadcast pressure.
How I build
I drop into established codebases, find what's slowing people down, and
build on top of them — using tools like Cursor and AI-assisted coding to
quickly understand complex systems, spot patterns, prototype ideas, and build lightweight features, then working with
engineers to test and release them reliably.
These are internal CNN production tools and are not shown publicly.
The diagrams below are simplified illustrations of the workflows — with the parts I
contribute to highlighted.
01 — Data Pipelines & Templates
Element Builder
A producer-facing tool for turning live data into consistent on-air
graphics. I extend it with new data sources and templates.
Where I plug in: new data sources (built as Airflow DAG
pipelines), new interactive graphic templates, and how-to docs & guides.
The problem
Live coverage constantly needs new datasets and new graphic formats.
The tool has to keep absorbing fresh data feeds and templates in a breaking news environment —
fast, and without breaking on air.
What I did
I bring new data sources and APIs into the tool as Airflow DAG pipelines,
and build new graphic templates and one-off special projects on top of the existing system —
so producers have the datapoints and formats they need for whatever story is breaking.
How I work
I pick up the existing codebase and pipeline setup quickly and build
within it — pairing with engineers to ship new data feeds and templates reliably, and
extending the tool as coverage needs evolve.
Docs & training
Wrote how-to guides and ran training sessions with teammates so
producers could confidently use the new templates and tools.
Impact
Kept the tool current with the stories being told, giving producers a
faster, more consistent way to turn data into graphics on air.
Svelte
JavaScript
REST APIs
Airflow
Python
Git
02 — Product Design & Prototyping
Producer Tools 2.0
A redesign of the content system producers use to manage CNN's Magic Wall Election App
— from clunky to intuitive.
My focus: redesigning the CMS interface and workflow —
the editorial-input step where producers actually work.
The problem
Producer Tools 1.0 — the system editorial teams use to manage on-air
election graphics — wasn't intuitive and was hard to use under deadline, slowing people
down exactly when accuracy and speed matter most.
What I did
With the team, we reimagined it as Producer Tools 2.0 — prototyping
the redesign in Figma and rethinking both the look and the functionality so the workflow made
sense to the people using it live.
How I work
I focused on the experience — identifying the usability problems,
designing and prototyping the solution, then partnering with engineers through build and
rigorous QA to get it right.
Documentation
Wrote how-to guides documenting the redesigned workflow and how it
works.
Impact
A clearer, more intuitive content tool that made managing on-air election
graphics faster and less error-prone.
Figma
Prototyping
UX Design
QA
Jira
Confluence
03 — Data, Features & QA
Magic Wall Election App
The flagship CNN Magic Wall Election App anchors use on election nights — I feed it data,
build lightweight features, and hammer on the QA.
Where I plug in: election data & analysis,
rehearsals & QA, and lightweight in-app features.
The problem
On election night, anchors need to explore results live — vote margins,
shifts, demographics — instantly and accurately, on air, in front of millions of live viewers with no room
for error.
What I did
I manage election and demographics data in the app — voting data,
win-margin analysis, and topical datasets spanning cost of living, health, and demographics —
run on-the-fly analysis during live coverage, and develop lightweight new features built around
that data.
How I work
I work on top of the existing app — learning the codebase and extending
it with data, analysis, and focused features, backed by rigorous QA before and during air.
Impact
Helped CNN viewers understand election results in real time through
accurate, clear, interactive graphics.
JavaScript
Python
SQL
Data Analysis
QA
D3
Git