ComboCurve is a industry leading cloud-based software solution for A&D, reservoir management, and forecasting in the energy sector. Our platform empowers professionals to evaluate assets, optimize workflows, and manage reserves efficiently, all in one integrated environment.

By streamlining data integration and enhancing collaboration, we help operators, engineers, and financial teams make informed decisions faster. Trusted by top energy companies, ComboCurve delivers real-time analytics and exceptional user support, with a world-class customer experience team that responds to inquiries in under 5 minutes.

We’re hiring a Senior Software Engineer to join our Economics Team. You’ll help design, build and maintain the calculation logic, data flows and infrastructure that power ComboCurve’s Economics engine. This role is ideal for someone who loves writing modern Python, caring about architecture and testability, and building new features that make ComboCurve’s platform even more powerful.

What You’ll Do 

1. Write efficient Python code on structured time series datasets that scales easily across cloud infrastructure.

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

1. Engage in software and infrastructure system design discussions, contributing to architectural decisions that shape the ComboCurve platform.

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

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

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

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

1. Collaborate with engineering peers through code reviews, technical documentation, and shared standards that raise code quality the team.

Requirements

Technical

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

1. API Design: Clean REST or gRPC services with OpenAPI contracts. Knows how to version and evolve APIs without breaking consumers.

1. Web Frameworks & Serving: Hands-on experience with Flask and/or FastAPI for building production services, and comfortable configuring Gunicorn for WSGI deployment.

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

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

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

1. SaaS Delivery: Proven track record taking features to production in cloud-based SaaS products. Comfortable with the full lifecycle from dev to deploy to monitor.

1. MongoDB: Schema design, indexing, and aggregation pipelines in production. ODM like MongoEngine or native driver, e.g. PyMongo.

1. Modern Dependency Management: Hands-on with uv or similar for fast package resolution and virtual environment handling.

1. Testing: Comprehensive pytest suites including fixtures, parameterization, and mocked external services.

1. Containerization: Docker and Docker Compose for local and production. Knows how to keep images lean.

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

Nice to Have

1. Google Cloud Platform: Deploying and managing services on GCP, specifically Cloud Run, Cloud Functions, and Cloud Storage.

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

1. Domain Knowledge: Experience in the oil and gas industry or a background in Petroleum Engineering; the context matters here and shapes better product decisions.

Workflow & Collaboration

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

1. Can translate ambiguous product requirements into concrete technical proposals.

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

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

While this is a fully remote position, there will be an in person New Hire Orientation.