Data Science & Research Engineer - ML Ops & Research Integration

Position: Data Science & Research Engineer - ML Ops & Research Integration (Contract)
Job Title:
Data Science & Research Engineer - ML Ops & Research Integration (Contract)
Location (On-site, Remote, or Hybrid?):
Fairfax, VA (Remote)
Contract Duration:
Contract until 12/31/2026

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 Signal
  1.
• 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., Data Robot) 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., Data Robot) 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.