Data Science & Research Engineer – Signal1 Integration (Contract)

Data Science & Research Engineer - ML Ops & Research Integration (Contract)
8 months
Remote

Senior BI/analytics engineering role responsible for building and enhancing
Signal1 integration data products and analytics assets. The role blends a Senior BI Developer
profile with modern tooling—developing governed DBT models and curated marts on Databricks
and delivering Power BI semantic models and dashboards. Work is focused on development-
stage build and initial validation for controlled deployment.
Key Responsibilities:
Design and implement net-new or significantly enhanced DBT models, curated
data marts, and governed tables/views supporting Signal1 onboarding and
enterprise analytics consumption.
Design and implement net-new or significantly enhanced DBT models, curated
data marts, and governed tables/views supporting Signal1 onboarding and
enterprise analytics consumption.
Develop transformations and data products on Databricks (notebooks/jobs, Delta
patterns) as part of defined build scope.
Build Power BI datasets/semantic models and dashboards tied to approved
deliverables; implement refresh, row-level security, and performance best
practices.
Partner directly with Signal1 to align candidate models for ingestion
(inputs/outputs, metadata, versioning, and governance requirements).
Configure and validate Signal1 monitoring for onboarded models (monitor
definitions, thresholds, alerting hooks, and initial baselines); support initial
monitoring sign-off.
Define and validate business metrics and reconciliation checks; retain initial
testing evidence and sign-off for releases.
Author data contracts, lineage, and documentation (definitions, ownership, SLAs)
required for governed deployment and reuse.
Build Power BI datasets/semantic models and dashboards tied to approved
deliverables; implement refresh, row-level security, and performance best
practices.
Define and validate business metrics and reconciliation checks; retain initial
testing evidence and sign-off for releases.
Author data contracts, lineage, and documentation (definitions, ownership, SLAs)
required for governed deployment and reuse.

Partner with Data Science, Product, and platform owners to align data products to
downstream monitoring, governance, and stakeholder needs.
Develop and validate SQL-based performance metrics and supporting logic as
part of initial delivery and acceptance testing.
Create data contracts and documentation (lineage notes, definitions, quality
checks) required for governed deployment.
Partner with Data Science and platform owners to integrate ingestion assets with
downstream monitoring/governance workflows.
Support initial release readiness by producing validation queries and evidence that
ingestion outputs meet requirements.
Required Qualifications:
5 years of experience as a BI Developer / Analytics Engineer / Data Engineer
delivering production reporting and curated datasets.
Advanced SQL skills (complex transformations, performance tuning,
reconciliation).
Hands-on experience with DBT (models, tests, documentation) and building
governed transformation layers.
Hands-on experience building in Databricks (notebooks/jobs; Lakehouse concepts
such as Delta).
Hands-on experience with Power BI (semantic models/datasets, DAX
fundamentals, dashboard development, refresh and security patterns).
Experience with version control and code review practices; ability to produce
clear technical documentation.
Hands-on experience with DBT (models, tests, documentation) and modern data
warehouse concepts.
Strong SQL skills; ability to create reusable, well-documented transformation
logic.
Experience with version control and code review practices.
Preferred Qualifications:
Experience defining metric layers/semantic models and governed KPI catalogs.
Experience supporting AI/ML data products (feature stores, model monitoring
inputs) or analytics governance programs.
Familiarity with data quality frameworks and operationalizing acceptance criteria.
Python experience for data tooling/automation.
Experience defining data quality checks and operationalizing acceptance criteria.

Familiarity with Python for data tooling/automation.
Deliverables / Success Measures: Deployed DBT models and curated marts on Databricks for
defined onboarding scope; Power BI semantic models/dashboards delivered with
documentation and security patterns; documented data contracts/lineage; initial validation
evidence for releases; reduced onboarding backlog for Signal1 integrations.


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