Sr. Python Engineer

Our customer is seeking an exceptional Senior Python Engineer to design and build the software backbone of the platform that powers our real-time analytics, AI/ML initiatives, and enterprise reporting for the automotive service industry. This is not a scripting role. We are looking for an engineer who treats Python as a first-class software engineering language: someone who designs layered, extensible systems, writes libraries other engineers build on, and can reason about concurrency, memory, performance, and maintainability at scale.

You will turn long-running, manual, and tribal-knowledge processes into well-architected, tested, and observable Python services and frameworks that run against high-volume operational data — on the order of hundreds of thousands of repair-order records per day — flowing through our AWS and Snowflake ecosystem.

Most important: deep, demonstrable Python mastery and a track record of architecting complex Python systems (frameworks, SDKs, distributed processing engines, or plugin-based platforms). We will trade breadth of data-platform tooling for depth in Python; Snowflake, Airflow, and AWS specifics can be learned on the job.

• Experience: 7+ years of professional software engineering, with at least 5 years building production systems primarily in Python.
• Python mastery (primary): Expert-level command of the language and its internals — data model, typing system, iterators/generators, decorators, descriptors, metaclasses, context managers, memory model, and the GIL. You can explain why, not just how.
• Complex Python architecture: Proven experience designing and owning large, multi-module Python codebases or frameworks used by other engineers — not only standalone scripts or notebooks.
• Concurrency & performance: Hands-on production experience with asyncio and/or multiprocessing, and a track record of profiling and measurably improving the speed or memory footprint of Python systems.
• Testing discipline: Deep experience with pytest and test design for complex systems, including mocking strategy, integration testing, and test-driven development.
• Data handling: Strong experience processing large datasets in Python (Polars, pandas, PyArrow, DuckDB, or similar), dataframe and schema validation (Pandera or Pydantic), and solid working SQL.
• Modern Python ecosystem: Current with Python 3.12+ and modern tooling — uv, ruff, pyright/mypy, Pydantic v2, async-native libraries (httpx, asyncpg/aiohttp) — and able to evaluate new runtime features critically.
• Engineering practices: Git-based workflows, CI/CD, packaging and dependency management, containerization, and strong documentation habits (design docs, READMEs, runbooks).
• Data platform exposure: Experience with Snowflake, Airflow (including custom operators/DAG factories), dbt, or Databricks/PySpark is preferred.
• AWS & event-driven systems: Hands-on use of S3, Lambda, SQS/SNS, Step Functions, or ECS from Python, including idempotent consumers for Kafka, Kinesis, or SQS is added advantage.
• Performance extensions: Experience with Cython, Rust bindings (PyO3), or Numba, or with running workloads on the free-threaded CPython build is added advantage.
• LLM & agent frameworks: Hands-on experience with MCP server development, LangGraph, Pydantic AI, or similar agent frameworks, and with LLM APIs such as Anthropic, OpenAI, or AWS Bedrock is added advantage.
• Automotive domain: Familiarity with repair-order, dealership fixed-operations, or DMS data is a plus.

Because this role centers on Python architecture, we ask every candidate to include a short write-up (one page or less) or a link to code that answers the following:

• A complex Python system you architected: What problem did it solve, how was it structured (modules, layers, key abstractions), and why did you choose that design over the alternatives
• Scale and performance: What were the volume, concurrency, or latency constraints, and how did you profile and optimize the system to meet them
• Extensibility and quality: How did other engineers extend your code, how did you keep it tested and maintainable, and what would you design differently today
• AI in your workflow: How do you use AI coding assistants or LLM-powered tooling in your engineering work, and how do you keep the output production-quality
• Shortlisted candidates should expect a technical interview that includes a live architecture discussion and an in-depth review of real Python code.