💻 Tech

Python Developer — CV Example

Backend specialist building scalable, high-performance microservices daily

What Does a Python Developer Actually Do?

Python Developers build and maintain the backend systems powering modern web and mobile applications. They specialise in server-side logic, database design, API development, and data processing pipelines using frameworks like Django, FastAPI, and Flask. Most work within agile teams at tech companies like Stripe, Deliveroo, or Wise, reporting to Engineering Managers or Tech Leads. Core daily activities include writing async Python code, designing REST APIs, optimising database queries, managing background jobs with Celery, deploying to cloud platforms like AWS or GCP, and conducting code reviews. The role demands strong problem-solving skills, understanding of system design principles, and ability to handle mission-critical production systems.

James Mitchell

Senior Python Developer

📍 London, UK✉️ james.mitchell@email.com

Summary

Senior Python Developer with 6+ years specialising in high-performance backend services, async architecture, and data pipeline development. Led Django and FastAPI microservices handling 50M+ daily requests at Stripe, reducing latency by 35% through optimised async patterns and implementing critical payment processing features.

Work Experience

Senior Python Developer at StripeApr 2022 — Present

Architected FastAPI microservice handling 50M+ daily requests, reducing P99 latency by 35% through async/await optimisation and connection pooling.
Led team of 4 junior developers building payment reconciliation pipeline processing 2TB+ daily data using Celery, Kafka, and PostgreSQL.

Python Developer at DeliverooJun 2020 — Apr 2022

Built Django REST API serving 15M monthly active users, implementing real-time order tracking with WebSockets reducing customer latency by 55%.
Developed Apache Airflow data pipeline processing 500K+ daily restaurant orders, enabling ML team to build recommendation engine increasing order frequency by 18%.

Skills

Python (Expert)FastAPIDjango & Django REST FrameworkFlaskAsync/Await & AsyncioPostgreSQL & SQL OptimizationRedis & Caching StrategiesCelery & Message QueuesApache AirflowDocker & KubernetesAWS (EC2, RDS, S3, Lambda)Git & CI/CD PipelinesPytest & Unit Testing

What Recruiters Look For

Recruiters prioritise 3+ years backend Python experience, proven expertise with Django or FastAPI, and solid understanding of async programming patterns. They seek evidence of shipping production systems at scale—metrics matter: request volumes handled, latency improvements achieved, uptime percentages. Strong SQL optimisation and database design knowledge are essential, as is familiarity with cloud platforms (AWS/GCP preferred). They value engineers who've led technical initiatives, mentored juniors, or reduced technical debt measurably. Code quality indicators—high test coverage, clean architecture, code review discipline—differentiate candidates. Finally, communication skills and ability to collaborate across teams are increasingly important for mid-level and senior roles.

Key Skills to Include

Core technical skills: Python (obviously), FastAPI, Django/Flask, async/await patterns, PostgreSQL and advanced SQL, Redis, Celery, Docker, Git. Cloud platforms: AWS (EC2, RDS, Lambda, S3) or GCP equivalents. Data tools: Apache Airflow, Kafka, or pandas for pipeline work. Testing: pytest, unittest mocking. Supporting skills: REST API design, microservices architecture, CI/CD pipelines, basic Linux, and shell scripting. Domain knowledge strengthens applications—e.g., fintech (payment processing), logistics (real-time tracking), or e-commerce (scalability). Soft skills essential: problem-solving, debugging methodology, and communication. Include any DevOps exposure (Kubernetes, monitoring tools) as bonus differentiators for senior positions.

Common Mistakes

Avoid vague bullet points like 'worked with Django'—quantify impact ('reduced API latency by 35% implementing async caching'). Don't list every tool you've touched; focus on depth in 5-6 core technologies. Generic summaries fail to stand out; explicitly mention your specialisation (backend systems, data pipelines, async architecture). Many developers understate infrastructure contributions—deployment, monitoring, incident response matter to employers. Don't claim expertise in 15+ languages; depth beats breadth. Forgetting to mention scale (requests/second, data volume, user count) is a missed opportunity—context makes achievements credible. Finally, avoid outdated tech stacks (Python 2, old Django) unless genuinely relevant, and don't list certifications without backing them with real project experience.

Formatting Tips

Keep your CV to one page if under 5 years experience, two pages maximum if senior. Use a clean, readable font (Calibri, Arial) with clear hierarchy—bold company names and job titles, italicise dates. Start each bullet with a strong action verb (architected, optimised, implemented, designed). Structure bullets as: [action] + [what you did] + [measurable result]—never exceed 20 words. Use consistent date formatting (Mar 2022, not March 2022 or 03/22). Quantify ruthlessly: percentages, milliseconds, number of users, revenue impact. Group skills into categories (Languages, Frameworks, Cloud, Tools) for scannability. Use ATS-friendly formatting—no tables, columns, or graphics. Tailor the skills section to each job description, front-loading the most relevant technologies.

Average Salary — Python Developer

United States

$120,000 – $185,000

United Kingdom

£65,000 – £110,000

Germany

€60,000 – €95,000

UAE / Dubai

$100,000 – $160,000

Canada

CAD $90,000 – $145,000

Australia

AUD $95,000 – $155,000

Figures in USD. Ranges reflect mid-level experience (3–7 years). Senior roles and major metro areas typically sit at the top of these bands.

Top 5 Interview Questions — Python Developer

1Walk me through how you'd optimise a Django view that's currently taking 2 seconds to respond and serving 1000 requests/second.

I'd start by profiling with Django Debug Toolbar to identify bottlenecks—likely N+1 queries or missing database indexes. I'd add select_related/prefetch_related for ORM queries, implement Redis caching for frequently accessed data, and consider moving heavy computations to async tasks with Celery. For the database, I'd add appropriate indexes on foreign keys and frequently filtered columns. Finally, I'd measure improvements: target sub-500ms response times. If still slow, I'd consider moving to raw SQL or implementing pagination for large datasets.

2How do you handle async/await in Python? What are common pitfalls?

Async/await enables non-blocking I/O, crucial for high-concurrency systems. In FastAPI, I use async def route handlers with await for database calls and HTTP requests. Common pitfalls: mixing sync and async code (use thread_pool_executor as fallback), blocking operations inside async functions, and forgetting to await tasks. I've learned to use asyncio profiling to detect blocking calls. Testing async code requires pytest-asyncio. Proper implementation reduced our API latency by 35% while handling 3x more concurrent users.

3Describe your experience with data pipelines and how you'd design one handling 500K daily records.

I've built Apache Airflow pipelines orchestrating complex ETL workflows. For 500K daily records, I'd design: Python scripts reading from source (API/database), transforming with Pandas or Polars, validating data quality, then loading to data warehouse. I'd use Celery for distributed processing, partition data by date for efficiency, and implement monitoring/alerting for failures. I've handled similar scale at Deliveroo, processing restaurant orders with 99.9% accuracy. Key: idempotency (retries don't duplicate), incremental loads, and backfill capabilities.

4Tell me about the most complex bug you've debugged in production. How did you resolve it?

At Stripe, a payment reconciliation service was losing transactions during high traffic. Using structured logging and tracing, I identified: async tasks weren't awaiting database commits before returning, causing race conditions. The fix: ensuring transaction context managers wrapped all database operations and adding explicit await for Celery task completion. I implemented comprehensive logging with correlation IDs and added circuit breakers for downstream services. This reduced data loss incidents by 100% and improved system reliability to 99.95% uptime.

5How do you approach writing testable Python code? What's your testing strategy?

I write testable code by keeping functions pure (single responsibility, no side effects), using dependency injection, and separating business logic from framework code. Strategy: unit tests for core logic using pytest (aiming 85%+ coverage), integration tests for database/API interactions with fixtures, and mock external dependencies. I use factories for test data consistency. At Deliveroo, we adopted test-driven development increasing coverage from 42% to 87%, reducing production bugs significantly. Fast test suites (parallel execution) are essential for rapid iteration.

How to Tailor Your CV

Companies like Stripe, Deliveroo, Wise, and Booking.com actively hire Python Developers for backend infrastructure, payments, and logistics systems. Tailor your CV by researching their tech stack—Stripe uses FastAPI for microservices, Deliveroo relies on Django for REST APIs, and Wise optimises PostgreSQL heavily. Highlight relevant experience: if applying to Stripe, emphasise high-concurrency async work; for Deliveroo, showcase real-time data processing; for Booking.com, demonstrate scale experience (millions of users). Use their terminology in your CV, mention open-source contributions if relevant to their stack, and prepare examples solving similar technical challenges at scale.

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