Senior Software Engineer, Python

Write efficient Python code on structured time series datasets thatscaleseasily across cloud infrastructure.

Own features end-to-end- from scoping and design through implementation, deployment, and monitoring-working, working as an independent unit alongside our Product Manager.

Engage in software and infrastructure system design discussions, contributing to architectural decisions that shape theCombo

Curveplatform.

Build andmaintainbackend services and APIs in Python that are reliable, well-tested, and straightforward to extend.

Model, query, andoptimizedata in MongoDB-schema design, indexing, and aggregation pipelines-so product features stay fast as data grows.

Deploy andoperatecontainerized services on cloud infrastructure, leveraging GCP components such as Cloud Run, Cloud Functions, and GCS.

Incorporate AI-first development practices-using AI tooling to accelerate delivery, improve code quality, and explore new product capabilities.

Collaborate with engineering peers through code reviews, technical documentation, and shared standards that raisecodequalitythe team.

Python: Production-grade Python 3.13+, type annotations and async/await as the default. No shortcuts on type safety.

API Design: Clean REST orgRPCservices with

OpenAPIcontracts.

Knowshow to version and evolve APIs without breaking consumers.

Web Frameworks & Serving: Hands-on experience with Flask and/orFastAPIfor building production services, and comfortable configuring

Gunicornfor WSGI deployment.

Software Architecture Patterns: SOLID principles and clean architecture in practice. Designs decoupled, maintainable services that scale.

Data & Statistical Analysis: Comfortable working with structured datasets in Python using tools like pandas ornumpyfor basic statistical analysis, exploratory analysis, and deriving actionable insights from data.

Data Processing & Visualization: Able to process medium-to-large datasets efficiently and communicate findings clearly through simple visualizations or reports when needed.

SaaS Delivery: Proven track recordtaking features to production in cloud-based SaaS products. Comfortable with the full lifecycle from dev to deploy tomonitor.

MongoDB: Schema design, indexing, and aggregation pipelines in production. ODM like

Mongo

Engineor native driver,e.g.PyMongo.

Modern Dependency Management: Hands-on withuvor similar for fast package resolution and virtual environment handling.

Testing: Comprehensivepytestsuites including fixtures, parameterization,and mockedexternal services.

Containerization: Docker and Docker Compose forlocal and production.

Knowshow to keep images lean.

Code Quality: Enforces standards via tools like ruff andpyright. Treats static analysis as a first-class concern.

Google Cloud Platform: Deploying and managing services on GCP, specifically Cloud Run, Cloud Functions, andCloud

Storage.

AI Integration: Exposure to LLM APIs or agent frameworks; ability to wire AI capabilities into product features without needing to be an ML specialist.

Domain
Knowledge :

Experience in the oil and gas industryor abackgroundin

Petroleum Engineering;the context matters here and shapes better product decisions.

Takes ownership end-to-end, from scoping to shipping to iterating.

Can translate ambiguous product requirements into concrete technical proposals.

Communicates tradeoffs clearly to both engineers and non-technical stakeholders.

Reviews code to raise quality and share context, not just approve.