Senior Data & Analytics Engineer, Domain Enablement
New
S
Shield AIDefense Tech
RemoteFull-TimeSenior
Salary120,000 - 180,000 USD per year
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Job Details
- Experience
- 5+ years
- Required Skills
- SQLData engineeringData modelingDatabricksPySpark
Requirements
- 5+ years of experience in analytics engineering, BI engineering, data engineering, or a hybrid role.
- Strong dimensional modeling and semantic design skills (facts, dimensions, grain, conformed dimensions).
- Strong SQL skills and comfort with modern cloud data platforms such as Databricks.
- Ability to translate ambiguous business requirements into precise, auditable, and reusable data models.
- Data engineering fluency for Silver-to-Gold transformations, testing, performance tuning, and production deployment.
- Ability to understand business meaning and usage constraints of data.
- Strong communication skills and comfort working directly with business stakeholders.
- Experience in cross-functional business domains such as finance, RevOps, marketing, or HR preferred.
- Experience building modular, tested transformation pipelines on Databricks (SQL/pyspark, Delta Live Tables) preferred.
- Experience with semantic layer tooling, governed metrics, or AI/BI consumption layers preferred.
- Experience in regulated or security-sensitive industries preferred.
Responsibilities
- Build and maintain Silver and Gold data models, domain marts, curated datasets, and semantic assets for priority domains onboarding to Databricks.
- Partner directly with business stakeholders to translate domain requirements and KPI definitions into governed, testable, and reusable transformation logic.
- Apply enterprise modeling standards, naming conventions, semantic definitions, and promotion rules.
- Create reusable domain patterns and analytical building blocks to enable team self-service.
- Support the design of semantic views and curated layers for consumption by BI tools and Databricks AI/BI experiences.
- Work across domain boundaries for overlapping or interacting metrics.
- Ensure data sensitivity and classification are reflected in modeling and semantic exposure.
- Review and refine partner-delivered data models for production-worthiness and alignment.
- Provide patterns, documentation, and technical guidance to support team growth into self-service analytics.
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