Python engineering teams
Scale your backend without the overhead. Vetted Python engineers.
When traffic spikes, your backend needs someone who can trace a Celery backlog at 2 a.m., ship an Alembic migration without breaking billing, and keep OpenAPI contracts honest. We staff Python engineers who write type-checked code, profile hot paths under real load, and design event-driven services that stay maintainable after the first release train. You get faster incident resolution and fewer Friday-night migration surprises.
Discuss your Python roadmapfrom fastapi import APIRouter, Depends, statusfrom sqlalchemy.ext.asyncio import AsyncSessionfrom app.db import get_sessionfrom app.schemas import InvoiceIn, InvoiceOut, JobStatusfrom workers.tasks import process_invoicerouter = APIRouter(prefix="/invoices", tags=["billing"])@router.post("", response_model=InvoiceOut, status_code=status.HTTP_202_ACCEPTED)async def create_invoice( payload: InvoiceIn, session: AsyncSession = Depends(get_session),) -> InvoiceOut: invoice = await InvoiceRepository(session).create_draft(payload) job = process_invoice.delay(invoice.id) await session.commit() return InvoiceOut(id=invoice.id, status=JobStatus.queued, job_id=job.id)Core stack
- FastAPI
- Django
- Celery
- Alembic
- SQLAlchemy
- Redis
5+
Average years in production Python
Senior and mid-level engineers, not fresh bootcamp grads learning on your sprint board.
Deep-Dive Tech Stack
Python backends succeed when the API layer, job queue, cache, and database evolve together. We match engineers on the full stack you run so concurrency, migrations, and memory tuning are handled as one system, not four disconnected specialties.
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FastAPI
Async REST and WebSocket APIs with OpenAPI-first contracts and thin handlers via dependency injection. Under WebSocket fan-out, blocking ORM calls and exhausted connection pools stall the event loop; our engineers use async drivers, bounded concurrency, and thread pools where CPU-bound work belongs off the hot path.
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Django
Admin-heavy products, multi-tenant auth, and ORM migrations at scale. They catch N+1 patterns in review, tune select_related before latency shows up in dashboards, and know when a modular monolith beats a premature microservice split. Migration rollback paths are documented before production tables change.
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Celery
Background jobs with idempotent task design, retry policies, and queue routing that survives spikes. When at-least-once delivery duplicates work, they debug poison messages, tune worker prefetch, and configure Redis or RabbitMQ with visibility into queue depth instead of fire-and-forget tasks.
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Alembic
Version-controlled migrations with reversible scripts and expand-contract patterns for zero-downtime deploys. Migrations are tested against staging snapshots and reviewed for lock contention on large tables; CI catches drift before merge, not after prod is already inconsistent.
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SQLAlchemy
Query layers and connection pools sized to your RDS limits, with lazy-loading pitfalls caught in review. Slow queries get EXPLAIN ANALYZE treatment, indexes ship with measured impact, and session lifecycle stays explicit in both async and sync code paths.
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Redis
Caching with TTL discipline, rate limiting, Celery broker setup, and pub/sub for lightweight fan-out. They watch eviction policies and hot keys so stale cache data surfaces in metrics before it becomes a customer ticket.
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pytest & typing
Fixture-driven integration suites with mypy or pyright in CI on critical paths. Type hints document contracts between services; tests catch regression when pydantic models or API schemas change across team boundaries.
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Pydantic & settings
Request and response models with validation at the boundary, pydantic-settings for typed config from env vars, and schema versioning when mobile clients lag API deploys. Invalid payloads fail fast with structured errors instead of corrupting downstream state.
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PostgreSQL & async drivers
asyncpg or psycopg3 with pool sizing against RDS limits, read-replica routing for reporting queries, and transaction boundaries explicit in service layers. Connection exhaustion under Celery fan-out gets diagnosed from pool metrics, not guessed from timeouts.
Numbers that matter to engineering leads
- Average years in production Python
- 5+
- Typical ramp to first merged PR
- 2–3 wks
- Engagement retention past 6 months
- 90%+
- Bootstrapped MVPs to production
- Weeks
Senior and mid-level engineers, not fresh bootcamp grads learning on your sprint board.
Onboarding includes repo access, CI setup, and a scoped starter ticket, not a month of shadowing.
Low churn because we match on stack depth and communication style upfront.
Teams we've staffed have shipped billing, auth, and core APIs inside a single quarter.
Straight answers before you sign
How do you handle time-zone crossovers?
We align overlap windows explicitly, usually 3–4 hours with US or EU core teams. Standups, PR reviews, and incident calls land in that window. Async updates go to Slack or your tracker with written context so nothing depends on someone being online at midnight.
What is your code review process?
Every PR gets a peer review from a senior engineer on our side before it hits your repo. We check for test coverage, migration safety, and API contract breaks. Your tech lead stays the final approver. We don't bypass your branch protections.
Can engineers join our existing sprint cadence?
Yes. They use your Jira, Linear, or GitHub Projects from day one. We don't impose a separate process layer. If you run two-week sprints with retro-driven priorities, they slot in like any other team member.
What happens if the fit isn't right?
We replace within two weeks at no additional sourcing fee. We'd rather swap early than let a mismatch burn a quarter of runway.
Who owns the IP and commits?
You do. All work is done in your repositories under your CLA or employment terms. We don't retain code or reuse your business logic across clients.
