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Why this grade This listing scored 39/100, which is an F. It lost the most ground on pay transparency. See the breakdown
- Description depth 20 / 20 How much the posting actually says about the work, measured in characters of real text.
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- Corroboration 10 / 10 Whether more than one source carries this listing.
- Freshness 4 / 15 How recently it was posted. Older postings are likelier to be filled or abandoned.
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- Pay transparency 0 / 25 A published salary range, worth more than any other single factor because it is what a candidate cannot find out without applying.
-10 Ghost-job penalty — Deducted for signals that this posting may not be a real, currently-open role — staleness, repeated relisting, or talent-pool language.
Every figure above is arithmetic over the posting itself — its salary field, its text, its age, its tags and how many sources carry it. How the grades work →
Headquarters: Remote
URL: https://www.toptal.com/
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
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Review the current forecasting platform architecture and identify areas for improvement
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Refactor existing Databricks, Python, and PySpark implementations
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Move business logic out of Databricks notebooks and into reusable Python modules or packages
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Improve separation of concerns between orchestration and core business logic
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Establish stronger engineering standards and help define what "good" looks like for the platform
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Implement or improve automated testing practices and validation mechanisms for forecasting workflows
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Build or improve monitoring and observability, increasing visibility into how predictions are generated
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Help monitor model behavior and operational health over time
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Improve reliability of scheduled training workflows, reducing manual intervention on failure
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Improve failure handling, retries, and overall workflow resilience
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Maintain and extend existing forecasting capabilities as needed
What You Bring
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Strong professional experience with Databricks, including workspaces, notebooks, scheduled workflows, and CI/CD processes
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Strong Python engineering experience, including designing reusable modules or packages
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Strong PySpark experience with production data pipelines or distributed data processing
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Experience refactoring production code and improving maintainability
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Familiarity with time series forecasting concepts and workflows
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Ability to understand and work effectively within an existing, unfamiliar codebase
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Experience improving software quality, testing strategy, and engineering standards
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Experience implementing automated testing practices
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Experience improving monitoring, observability, or operational visibility for production systems
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Strong judgment around technical debt, refactoring priorities, and maintainable architecture
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Ability to work with existing systems rather than only building from scratch
Why This Role
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Real production impact: Improve a system the business already relies on, not a proof-of-concept
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Engineering maturity focus: Bring testing, observability, and maintainability to a platform that's outgrown its current state
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Meaningful ownership: Help define engineering standards for the forecasting platform going forward
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Flexible location: Preference for Toronto or St. Louis, but open to remote consultants globally with North American working-hours overlap
How to Apply
Ready to bring engineering rigor to a production forecasting platform? Apply through Toptal here: https://www.toptal.com/talent/apply
Apply for this role Opens weworkremotely.com — the link as listed; we have not yet verified it is the employer's own page
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Where this listing came from
- 28 Jul 2026 We Work Remotely first sighting
- 29 Jul 2026 Company careers page employer ATS also listed the same day
Seen on 2 boards over 72 days.