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Senior Software Engineer, Python

ComboCurve
Posted 2 days ago, valid for 16 days
Location

Houston, TX, US

Salary

Competitive

Contract type

Full Time

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Sonic Summary

info
  • We are hiring a Senior Software Engineer to join our Economics Team, focusing on designing, building, and maintaining the calculation logic and infrastructure for ComboCurve's Economics engine.
  • The ideal candidate will have experience writing modern Python, with a strong emphasis on architecture, testability, and feature development.
  • Candidates should have at least 5 years of experience in software engineering, particularly with Python and cloud-based SaaS products.
  • The role offers a competitive salary of $130,000 to $160,000, depending on experience and qualifications.
  • This fully remote position requires ownership of features from scoping to deployment, with an in-person New Hire Orientation.

We’reĀ hiring a Senior Software Engineer to join ourĀ EconomicsĀ Team.Ā You’llĀ help design,Ā buildĀ andĀ maintainĀ theĀ calculationĀ logic,Ā dataĀ flowsĀ and infrastructureĀ that powerĀ ComboCurve’sĀ Economics engine. This role is ideal for someone who loves writing modern Python, caring about architecture and testability, and buildingĀ new featuresĀ that makeĀ ComboCurve’sĀ platformĀ evenĀ more powerful.

WhatĀ You’llĀ DoĀ 

  1. Write efficient Python code on structured time series datasets thatĀ scalesĀ 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 theĀ ComboCurveĀ platform.
  1. Build andĀ maintainĀ backend services and APIs in Python that are reliable, well-tested, and straightforward to extend.
  1. Model, query, andĀ optimizeĀ data in MongoDB—schema design, indexing, and aggregation pipelines—so product features stay fast as data grows.
  1. Deploy andĀ operateĀ 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 raiseĀ codeĀ qualityĀ the team.

Requirements

Technical

  1. Python:Ā Production-grade Python 3.13+, type annotations and async/await as the default. No shortcuts on type safety.
  1. API Design:Ā Clean REST orĀ gRPCĀ services withĀ OpenAPIĀ contracts.Ā KnowsĀ how to version and evolve APIs without breaking consumers.
  1. Web Frameworks & Serving:Ā Hands-on experience with Flask and/orĀ FastAPIĀ for building production services, and comfortable configuringĀ GunicornĀ for WSGI deployment.
  1. Software Architecture Patterns:Ā SOLID principles and clean architecture in practice. Designs decoupled, maintainable services that scale.
  1. Data & Statistical Analysis:Ā Comfortable working with structured datasets in Python using tools like pandas orĀ numpyĀ for basic statistical analysis, exploratory analysis, and deriving actionable insights from data.
  1. Data Processing & Visualization:Ā Able to process medium-to-large datasets efficiently and communicate findings clearly through simple visualizations or reports when needed.
  1. SaaS Delivery:Ā ProvenĀ track recordĀ taking features to production in cloud-based SaaS products. Comfortable with the full lifecycle from dev to deploy toĀ monitor.
  1. MongoDB:Ā Schema design, indexing, and aggregation pipelines in production. ODM likeĀ MongoEngineĀ or native driver,Ā e.g.Ā PyMongo.
  1. Modern Dependency Management:Ā Hands-on withĀ uvĀ or similar for fast package resolution and virtual environment handling.
  1. Testing:Ā ComprehensiveĀ pytestĀ suites including fixtures, parameterization,Ā and mockedĀ external services.
  1. Containerization:Ā Docker and Docker Compose forĀ local andĀ production.Ā KnowsĀ how to keep images lean.
  1. Code Quality:Ā Enforces standards via tools like ruff andĀ pyright. Treats static analysis as a first-class concern.

Nice to Have

  1. Google Cloud Platform:Ā Deploying and managing services on GCP, specifically Cloud Run, Cloud Functions, andĀ CloudĀ Storage.
  1. AI Integration:Ā Exposure to LLM APIs or agent frameworks; ability to wire AI capabilities into product features without needing to be an ML specialist.
  1. DomainĀ Knowledge:Ā ExperienceĀ in the oil and gas industryĀ or aĀ backgroundĀ inĀ Petroleum Engineering;Ā the context matters here and shapes better product decisions.

Workflow & Collaboration

  1. TakesĀ 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 inĀ personĀ New Hire Orientation.




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