Headquarters: Remote
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About the Role

We're looking for a Senior Data/ML Engineer to refactor, operationalize, and improve an existing time series forecasting platform that's already live and driving real business value. This is not a greenfield build — the focus is modernizing a Databricks-based forecasting system that's been maintained primarily by a single developer for years. You'll reduce technical debt, strengthen testing and observability, and raise the engineering bar on a system the business already depends on. If you'd rather bring discipline and maturity to an existing production system than start from a blank slate, this is built for that.

What You'll Do

- Review the current forecasting platform architecture and identify areas for improvement

- Refactor existing Databricks, Python, and PySpark implementations

- Move business logic out of Databricks notebooks and into reusable Python modules or packages

- Improve separation of concerns between orchestration and core business logic

- Establish stronger engineering standards and help define what "positive" looks like for the platform

- Implement or improve automated testing practices and validation mechanisms for forecasting workflows

- Build or improve monitoring and observability, increasing visibility into how predictions are generated

- Help monitor model behavior and operational health over time

- Improve reliability of scheduled training workflows, reducing manual intervention on failure

- Improve failure handling, retries, and overall workflow resilience





- Maintain and extend existing forecasting capabilities as needed

What You Bring

- Strong skilled experience with Databricks, including workspaces, notebooks, scheduled workflows, and CI/CD processes

- Strong Python engineering experience, including designing reusable modules or packages

- Strong PySpark experience with production data pipelines or distributed data processing

- Experience refactoring production code and improving maintainability

- Familiarity with time series forecasting concepts and workflows

- Ability to understand and work effectively within an existing, unfamiliar codebase

- Experience improving software quality, testing strategy, and engineering standards

- Experience implementing automated testing practices

- Experience improving monitoring, observability, or operational visibility for production systems

- Strong judgment around technical debt, refactoring priorities, and maintainable architecture

- Ability to work with existing systems rather than only building from scratch

Why This Role

- Real production impact: Improve a system the business already relies on, not a proof-of-concept

- Engineering maturity focus: Bring testing, observability, and maintainability to a platform that’s outgrown its current state

- Meaningful ownership: Help define engineering standards for the forecasting platform going forward

- Flexible location: Preference for Toronto or St. Louis, but open to remote consultants globally with North American working-hours overlap

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📌 Senior Data/ML Engineer — Databricks Forecasting Platform | Remote (Toronto)
🏢 Neura Market
📍 Toronto