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Data Science & Research Engineer – ML Ops & Research Integration (Contract)
100% Remote
8 Months
Job Summary:
Senior BI/analytics engineering role supporting predictive model delivery and research integrations. The role blends a Senior BI Developer profile with modern tooling—building governed data products on Databricks, developing DBT transformation layers, and delivering Power BI semantic models/dashboards—alongside reusable integration components for ML Ops workflows. Work is focused on development-stage build and initial validation required for controlled go-live.
Key Responsibilities:
Develop and enhance data/analytics products that operationalize model inputs/outputs using DBT and Databricks (notebooks/jobs, Delta patterns).
Develop and enhance data/analytics products that operationalize model inputs/outputs using DBT and Databricks (notebooks/jobs, Delta patterns).
Build Power BI datasets/semantic models and dashboards tied to approved deliverables; implement refresh, security, and performance best practices.
Deliver predictive model and integration enhancements (Python/SQL) as part of defined build scope; support initial validation/testing evidence.
Align models for ingestion into Signal1 when applicable (outputs/metadata/versioning) and coordinate ingestion requirements with Signal1.
Build monitoring enablement as part of defined enhancements (monitor definitions, thresholds, alerting hooks, baseline calculations) and complete initial validation/sign-off.
Build or enhance ML Ops workflows (deployment automation, reproducible pipelines) and provide initial validation/testing required for release readiness.
Develop APIs and connectors for research platforms and datasets as reusable integration assets.
Produce technical documentation (interfaces, pipeline specs, metric definitions, deployment notes) supporting traceability and controlled release.
Deliver predictive model and integration enhancements (Python/SQL) as part of defined build scope; support initial validation/testing evidence.
Build or enhance ML Ops workflows (deployment automation, reproducible pipelines) and provide initial validation/testing required for release readiness.
Develop APIs and connectors for research platforms and datasets as reusable integration assets.
Produce technical documentation (interfaces, pipeline specs, metric definitions, deployment notes) supporting traceability and controlled release.
Build or enhance ML Ops workflows (deployment automation, reproducible pipelines, connector patterns) and provide initial validation/testing evidence.
Develop APIs and connectors to integrate research platforms and datasets as reusable assets.
Partner with Data Science and platform owners to ensure integration designs meet
governance, security, and release readiness requirements.
Produce technical documentation (interface definitions, pipeline specs, deployment notes) supporting traceability and controlled release.
Required Qualifications:
5 years of experience delivering BI/analytics solutions and/or data engineering pipelines in production.
Strong Python and SQL skills; experience building reliable, testable data transformations.
Hands-on experience with Databricks (notebooks/jobs) and Lakehouse concepts (e.g., Delta).
Hands-on experience with DBT (models, tests, documentation) and governed transformation patterns.
Hands-on experience with Power BI (semantic models/datasets, dashboard development, refresh/security patterns).
Experience with version control, code review, and CI/CD-aligned development practices.
Strong Python skills and comfort working with APIs and data services.
Solid SQL skills and experience validating data outputs against acceptance criteria.
Experience with version control, code review, and CI/CD-aligned development practices.
Preferred Qualifications:
Experience with ML lifecycle or analytics governance (feature store concepts, model monitoring inputs, KPI catalogs).
Experience integrating with managed ML platforms (e.g., DataRobot) or similar deployment environments.
Experience designing APIs/integration patterns in enterprise environments.
Familiarity with regulated or governed environments requiring documentation and change control.
Experience integrating with managed ML platforms (e.g., DataRobot) or similar model deployment environments.
Familiarity with data governance and controlled release processes in enterprise settings.
Deliverables / Success Measures:
Delivered Databricks/DBT data products aligned to scope; Power BI semantic models/dashboards delivered with documentation and security patterns; reusable APIs/connectors for research integrations; initial validation/testing evidence supporting go-live; reduced cycle time for model and research integrations.
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