This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data & Analytics Engineer, Domain Enablement (R5536) based in United States.
This role is a hybrid data and analytics engineering position focused on enabling business domains through a modern Databricks data platform.
You will build governed Silver and Gold data assets, reusable semantic models, and domain-ready analytical datasets.
The initial focus will span areas such as Accounting, Program Finance, Revenue Operations, Marketing, and HR.
You will work closely with business stakeholders to turn evolving requirements and KPI definitions into trusted, reusable data products.
The role combines strong technical expertise in data modeling and transformation with the ability to understand business context and data usage constraints.
You will help establish scalable patterns that increase self-service analytics while maintaining enterprise-wide consistency and governance.
As the platform evolves, you may also contribute to more technically complex domains such as Product and Engineering.
Accountabilities
- 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.
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.
Benefits
- Competitive salary based on skills, experience, certifications, and work location.
- Bonus opportunities for eligible full-time employees.
- Comprehensive employee benefits package.
- Equity opportunities for eligible full-time employees.
- Temporary employee benefits package available after 60 days, where applicable.
- Opportunities to work on modern data platforms and enterprise-scale analytics initiatives.
- A collaborative environment focused on building governed, reusable, and high-impact data capabilities.
- Reasonable workplace accommodations available for qualified individuals with disabilities or special needs.
- Employment offers are contingent on successful background checks and, where applicable, reference checks.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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