Python Programming Jobs in Fintech Roles and Responsibilities

from decimal import Decimal, ROUND_HALF_UP

# Never use float for money
print(0.1 + 0.2)                       # 0.30000000000000004

price = Decimal("199.99")
qty = 3
gst_rate = Decimal("0.18")
subtotal = price * qty
gst = (subtotal * gst_rate).quantize(Decimal("0.01"), rounding=ROUND_HALF_UP)
total = subtotal + gst
print(subtotal, gst, total)            # 599.97 107.99 707.96

Enjoy building APIs and systems?      -> Backend developer
Enjoy numbers, reports, accuracy?     -> Data analyst / engineer
Enjoy statistics and patterns?        -> Risk / fraud analyst
Enjoy finding bugs in critical flows? -> QA automation engineer

# FastAPI sketch: process each payment request only once
from fastapi import FastAPI, Header
from decimal import Decimal

app = FastAPI()
processed = {}   # use a database table with a unique key in real systems

@app.post("/payments")
def create_payment(amount: Decimal, to_account: str,
                   idempotency_key: str = Header(...)):
    if idempotency_key in processed:
        return processed[idempotency_key]      # same result, no new payment
    result = {"status": "accepted", "amount": str(amount), "to": to_account}
    processed[idempotency_key] = result
    return result

# Django: move money between wallets atomically
from django.db import transaction
from django.db.models import F

def transfer(from_id, to_id, amount):
    with transaction.atomic():
        sender = Wallet.objects.select_for_update().get(id=from_id)
        if sender.balance < amount:
            raise ValueError("Insufficient balance")
        Wallet.objects.filter(id=from_id).update(balance=F("balance") - amount)
        Wallet.objects.filter(id=to_id).update(balance=F("balance") + amount)
        LedgerEntry.objects.create(wallet_id=from_id, amount=-amount)
        LedgerEntry.objects.create(wallet_id=to_id, amount=amount)

import pandas as pd

ours = pd.read_csv("internal_txns.csv", dtype={"txn_id": "string"})
bank = pd.read_csv("bank_settlement.csv", dtype={"txn_id": "string"})

merged = ours.merge(bank, on="txn_id", how="outer",
                    suffixes=("_ours", "_bank"), indicator=True)
missing_at_bank = merged[merged["_merge"] == "left_only"]
missing_internally = merged[merged["_merge"] == "right_only"]
both = merged[merged["_merge"] == "both"]
amount_mismatch = both[both["amount_ours"].round(2) != both["amount_bank"].round(2)]

print(len(missing_at_bank), len(missing_internally), len(amount_mismatch))

import pandas as pd
from sklearn.linear_model import LogisticRegression

X = loans[["income_lakh", "existing_emis", "credit_history_years"]]
y = loans["defaulted"]
model = LogisticRegression(max_iter=1000).fit(X, y)

coefs = pd.Series(model.coef_[0], index=X.columns).sort_values()
print(coefs)
# Negative coefficient: feature lowers predicted default risk
# Positive coefficient: feature raises it - easy to explain

All roles: Python core, SQL, Git, testing basics
Backend:   + Django or FastAPI, transactions, auth, Decimal
Data:      + pandas, SQL window functions, reconciliation
Risk:      + statistics, scikit-learn, model explanation
QA:        + pytest, API testing, payment edge cases
Then:      one fintech project + interview practice

Backend: Wallet API with idempotent payments and a ledger
Data:    Daily settlement reconciliation with exception report
Risk:    Explainable loan-default model on public data
QA:      Automated tests for a mock payment API
Use only public or synthetic data - never real customer data.

Common fintech Python interview topics
- Why not float for money? (Decimal / integer paise)
- What is idempotency? Give an example
- How do database transactions prevent bad states?
- How would you reconcile two transaction files?
- How do you explain a model's decision to a regulator?
- How do you protect sensitive financial data?