Prebuilt Projects2Jobs roadmap
Algorithmic Trading Engineer Roadmap
Build hiring proof for Algorithmic Trading Engineer roles: financial data, modeling, and rigorous validation.
Algorithmic Trading Engineer candidates and career switchers targeting Finance / FinTech roles.
Timeline
8 weeks
Level
Intermediate
Final outcome
A Algorithmic Trading Engineer portfolio with shipped projects, public GitHub proof, resume bullets, and interview talking points.
Skills to prove
Python
Pandas/NumPy
Financial math
Backtesting
Market data APIs
Risk metrics
Portfolio projects
- Backtesting engine with transaction costs and risk metrics
- Market data pipeline with storage and quality checks
- Pricing or risk model with a validation writeup
Prebuilt build path
Follow these phases in order. Each one ends with a portfolio artifact you can show in GitHub, on your resume, or in interviews.
Step 1
Weeks 1-2
Build the data backbone
Financial work is only as good as its data.
- Ingest market or financial data via API with validation and storage.
- Compute core metrics and document data quality issues.
Deliverable: A reliable financial data pipeline.
Step 2
Weeks 3-6
Model and validate
Prove quantitative rigor, not just promising charts.
- Implement a strategy backtest or pricing model with honest assumptions.
- Stress test it, report risk metrics, and document the limitations.
Deliverable: A validated quantitative project with a professional writeup.
Step 3
Weeks 7-8
Package the proof for hiring
Turn the work into evidence recruiters and interviewers can verify quickly.
- Polish each repo README with screenshots, setup steps, architecture notes, and tradeoffs.
- Write resume bullets and interview talking points that map each project to Algorithmic Trading Engineer job requirements.
Deliverable: Public GitHub proof, an updated resume, and interview-ready project stories.
Make it personal
Projects2Jobs compares this roadmap to your resume, current skills, and existing projects, then generates a role-specific build plan.
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