Data Analyst Career Inside the Fintech Industry Explained

A typical day for a fintech data analyst:

9:00 — Check dashboards and alerts
   - Payment success rate
   - Fraud flags
   - Support volume

10:00 — Standup with product and risk teams
   - What changed yesterday?
   - What needs investigation?

11:00 — Deep work
   - Write SQL queries
   - Investigate an anomaly
   - Build a report or cohort view

1:00 — Lunch

2:00 — Meet stakeholders
   - Present findings
   - Discuss next steps
   - Align on metrics

4:00 — Build or update a dashboard
   - Make it easier for the team
   - Reduce future questions

5:00 — Document findings
   - Write up what you learned
   - Share with the team

Questions you will answer often:

1. Why did payment success rate drop last week?
2. Which merchant segment is causing the most refunds?
3. What patterns show up in flagged fraud cases?
4. Which customer cohorts are churning fastest?
5. What is the cost per support ticket by category?
6. Which loan products have the highest default rate?
7. Where are customers dropping off in onboarding?

These questions all start with data.
Your job is to turn the data into a clear answer.

-- Payment success rate by day
SELECT
  DATE(created_at) AS day,
  COUNT(*) AS total,
  SUM(CASE WHEN status = 'success' THEN 1 ELSE 0 END) AS success,
  ROUND(
    100.0 * SUM(CASE WHEN status = 'success' THEN 1 ELSE 0 END) / COUNT(*),
    2
  ) AS success_rate_pct
FROM payments
WHERE created_at >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY 1
ORDER BY 1;

-- Flagged transactions by reason
SELECT
  reason_code,
  COUNT(*) AS flagged,
  ROUND(AVG(amount), 2) AS avg_amount,
  COUNT(DISTINCT user_id) AS unique_users
FROM fraud_flags
WHERE created_at >= CURRENT_DATE - INTERVAL '7 days'
GROUP BY reason_code
ORDER BY flagged DESC;

-- This tells you where the risk team
-- should focus their review effort.

-- Monthly retention by signup cohort
WITH signups AS (
  SELECT user_id,
         DATE_TRUNC('month', created_at) AS cohort
  FROM users
),
activity AS (
  SELECT user_id,
         DATE_TRUNC('month', event_at) AS active_month
  FROM events
  WHERE event_name = 'app_open'
)
SELECT
  s.cohort,
  a.active_month,
  COUNT(DISTINCT s.user_id) AS users
FROM signups s
JOIN activity a USING (user_id)
GROUP BY 1, 2
ORDER BY 1, 2;

SQL checklist for fintech:

□ SELECT, WHERE, JOIN
□ GROUP BY and HAVING
□ Subqueries and CTEs
□ Window functions (ROW_NUMBER, LAG, SUM OVER)
□ Cohort analysis patterns
□ Basic query performance awareness
□ Handling timestamps and time zones
□ Working with NULLs safely

Practice on:
- Transactions
- User signups
- Fraud flags
- Loan repayments

Python checklist for fintech:

□ pandas for loading and cleaning data
□ Grouping and aggregation
□ Merging datasets
□ Handling dates and time zones
□ Basic plotting (matplotlib, plotly)
□ Writing reusable small functions
□ Running scripts on a schedule

You do not need heavy ML.
You need clean analysis and automation.

Communication checklist:

□ Can you explain the metric in one sentence?
□ Can you show the trend in one chart?
□ Can you name the likely cause?
□ Can you state what you would do next?
□ Can you avoid jargon for non-technical teams?

Fintech teams value clarity.
A clear analyst is more valuable
than a clever one.

Project 1 — Payment health dashboard

Goal:
Build a dashboard that shows how healthy a payment
system is over time.

Data:
- Synthetic or public payment dataset
- Columns: timestamp, merchant, amount, status, reason

Analysis:
- Daily success rate
- Top failure reasons
- Success rate by merchant segment
- Latency distribution

Output:
- SQL queries in a clean file
- A dashboard (Metabase, Power BI, or Streamlit)
- A one-page writeup: what you found and why

What it shows:
- SQL fluency
- Metric thinking
- Clear communication

Project 2 — Fraud review analysis

Goal:
Analyse flagged transactions and recommend how to
prioritise review effort.

Data:
- Synthetic or public fraud dataset
- Columns: timestamp, user, amount, reason, outcome

Analysis:
- Volume of flags by reason code
- Outcome distribution by segment
- Which rules produce most false positives
- Time-of-day patterns

Output:
- Analysis notebook
- A clear summary with 3 recommendations

What it shows:
- Risk domain sense
- Investigation skill
- Judgment and prioritisation

Portfolio checklist for fintech analyst roles:

□ Two end-to-end projects
□ Clean READMEs with problem and findings
□ SQL files you can explain line by line
□ Dashboards or notebooks included
□ One story about a bug you found
□ One story about a metric you improved
□ Clear, short writeups

Hiring managers look for:
- SQL confidence
- Domain awareness
- Clear thinking
- Communication