Amelia Stracke

Why this persona fits the audience

She is a data analyst in a 7-person product team in London, preparing weekly summaries and ad hoc analysis in spreadsheets, Metabase, Slack, and ChatGPT. She is comfortable with numbers but only lightly technical, and has mostly stayed with free speech-to-text and browser tools.

• Age: 29
• Location: London, UK
• Occupation: Data analyst
• Income: £58k salary
• Education: MSc in Data Science
• Company size: 7-person startup
• Years in role: 3

“I don't need to be the most technical person in the room - I just need to be the most *reliable* one, and that's actually much harder.”

Amelia Stracke graduated with an MSc in Data Science from UCL at 26, only to quickly discover that 'data science' in a 7-person startup meant 'spreadsheet triage.' Now 29, she's the de facto data translator for a product team that treats analytics as an afterthought. Her days blend deep concentration on messy datasets with sudden firefighting when someone realizes the reports are wrong. She's developed an uncanny ability to spot inconsistencies in raw data, though she'll spend hours double-checking before pointing them out. Her MSc taught her advanced modeling, but her CEO only cares about 'weekly user growth charts.' This disconnect has left her stuck in a role that feels like a 'technical middle management' purgatory. She's tried to implement proper tools, but the team's 'freemium mindset' makes her feel like she's asking for too much. Recently, she's been sneaking AI tools into her workflow, finding that ChatGPT helps her query data more confidently - though she still writes detailed 'I'm not sure this is right' disclaimers on her analyses. Outside work, her flat is a temple to organization that contrasts sharply with her chaotic inbox. She's currently on a 'no new notebooks' vow, and equally committed to learning Python despite having three dormant accounts. Her ideal day would involve sunlight, proper data tools, and someone else asking for the metrics. She's both deeply competent and quietly terrified she'll be exposed as a fraud the moment she does something advanced.

Background & daily life

A day in their life

Amelia's day begins at 6:30am with a notebook, black coffee, and summarizing yesterday's Numbers achieves via voice memo. By 8:00, she's struggling through a Metabase dashboard that keeps rendering incorrectly. The team stands up around 10:30, where she mentally prepares for her one contribution: 'The Berlin dashboard is still broken.' Lunch is a sad lunchbox but worth it to avoid the kitchen's endless discussions about crypto. Afternoon is spent wrestling with a spreadsheet that won't sort properly, interspersed with Slack messages from the CEO asking 'What would happen if we...' about some pivot table. At 18:30, she's the last one in the office, painstakingly documenting a query she'll never use again. On the tube home, she scrolls through data viz blogs while silently practicing what she'll say at tomorrow's meeting when they ask for metrics again.

Lifestyle

Amelia rents a compact but well-organized flat overlooking a Camden street market, where she buys most of her secondhand clothes. Her weekends rotate between data visualization side projects, all-day library reading sessions, and long walks along the Regent's Canal. She's diligent about meal prepping (lentil dishes, roasted vegetables) but isn't above a 3am Pret a Manger run after work. Socially, she maintains a small circle of MSc classmates through a monthly book club (currently: *The Order of Time* by Carlo Rovelli). She's been trying to learn Python for over a year but keeps getting sidetracked by spreadsheet automation hacks. Financially, she's saving for a 'two-year sabbatical fund' but currently spends £20/month on fancy notebooks she doesn't use.

Work life

At work, Amelia's role is the tiny pivot point between their product team's chaos and the investor reports their CEO demands. Every Tuesday, she fights with Metabase until it produces the weekly engagement metrics, then spends the rest of the week reacting to Slack messages like 'What were the conversion rates last quarter in Berlin?' Her team's tools are a patchwork of free trials and workarounds. She relies heavily on ChatGPT for querying their messy Snowflake database when SQL intimidates her. The biggest pressure isn't the work itself, but the constant feeling that she's not technical enough for her title. She's currently debating whether to quietly learn R or just accept that she's the 'human backbone' of their data efforts.

Relationships

Amelia's closest connections are with three college friends who constantly argue about whether data science is 'real science.' Her parents, both secondary school teachers in Nottingham, gently question why she still works at a 'little tech company' after her 'fancy degree.' She avoids office banter but occasionally bonds with the sole other introvert on the team over shared disdain for Zoom calls. Her book club friends are her main sounding board, though they can't really understand why she's 'so stressed out about numbers.' She has a soft spot for the junior designer who always brings her weak green tea and never asks about her weekend.

How they decide

Amelia is the most research-heavy person you'll ever meet about spreadsheets. She'll spend four hours comparing Keyboard Maestro alternatives before abandoning all of them. When faced with technical decisions, she makes detailed pros/cons lists but often defaults to the option that feels most familiar. She rarely buys anything on impulse, though she has a weak spot for vintage Dragon NaturallySpeaking manuals.

How they sound

Amelia speaks in precise, measured sentences, often pausing to rearrange her thoughts. She avoids jargon unless absolutely necessary, and has a habit of saying 'I'm not sure that's technically correct' about things others would assert confidently. When explaining data, she'll apologize for her 'bad math voice' - which is actually very good, but she feels it needs to be more 'professional.' She never says 'crazy' or 'awesome,' using 'quite intense' and 'rather effective' instead. Her email tone is more formal than her Slack persona, where she occasionally expresses frustration in brackets (e.g. 'This report is (very much) not ready').