Senior Data & Analytics Engineer, Domain Enablement
New
J
JobgetherData & Analytics
USFull-TimeSenior
SalaryCompetitive salary based on skills, experience, certifications, and work location.
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Job Details
- Experience
- 5+ years of experience
- Required Skills
- SQLData modelingDatabricksPySpark
Requirements
- 5+ years of experience in analytics engineering, BI engineering, data engineering, or a hybrid role combining data modeling and transformation.
- Strong knowledge of dimensional modeling and semantic design, including facts, dimensions, data grain, conformed dimensions, and business-oriented analytical structures.
- Advanced SQL skills and experience working with modern cloud data platforms, particularly Databricks.
- Ability to translate ambiguous business requirements into precise, auditable, reusable, and maintainable data models.
- Strong data engineering knowledge, including Silver-to-Gold transformations, data testing, performance optimization, and production deployment practices.
- Ability to understand the business meaning, context, and usage restrictions associated with data rather than focusing solely on technical transformations.
- Excellent communication and stakeholder-management skills, with the ability to work effectively with business teams whose priorities and definitions may evolve.
- Experience in finance, program finance, Revenue Operations, marketing analytics, HR analytics, product analytics, or another cross-functional business domain is preferred.
- Experience developing modular and tested transformation pipelines using Databricks SQL, PySpark, Delta Live Tables, or equivalent technologies is a plus.
- Experience with semantic-layer technologies, governed metrics, or AI/BI consumption layers is preferred.
- Experience working in regulated or security-sensitive environments is advantageous.
- Willingness and ability to expand from G&A and GTM-focused domains into areas such as Product or Engineering as organizational needs evolve.
Responsibilities
- Build and maintain Silver and Gold data models, domain marts, curated datasets, and semantic assets for business domains being onboarded to Databricks.
- Partner directly with stakeholders to translate business requirements, metrics, and KPI definitions into governed, testable, and reusable transformation logic.
- Apply enterprise data modeling standards, naming conventions, semantic definitions, and promotion rules while identifying opportunities to improve those standards.
- Develop reusable analytical patterns and data building blocks that enable teams such as FP&A, Revenue Operations, and Marketing to become increasingly self-service.
- Design semantic views and curated data layers that can support BI tools, Databricks SQL, and AI/BI experiences such as Genie.
- Work across domain boundaries where metrics and concepts intersect, including G&A, GTM, workforce, and product-adjacent data.
- Incorporate data sensitivity, classification, governance, and approved-use requirements into modeling decisions, joins, and semantic data exposure.
- Review and improve partner-delivered or domain-contributed models to ensure they are production-ready, understandable, reusable, and aligned with enterprise definitions.
- Provide documentation, examples, technical guidance, and reusable patterns that help domain teams develop stronger self-service analytics capabilities without unnecessarily centralizing every request.
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