[Hiring] Senior Analytics Engineer @Niche

Role Description

We are looking for a Senior Analytics Engineer to join the Data Engineering team. You’ll architect, build, and scale our enterprise data models, translating complex raw data into clean, business-ready datasets while driving data accuracy, comprehensive documentation, observability, semantic consistency, and optimal warehouse performance. You will lead the design and maintenance of modular dbt models, semantic layers, and BI views for consumption by business analysts, data scientists, and executive stakeholders. This is an exciting opportunity to act as a technical leader on our team as we build the next generation of our analytics platform and semantic capabilities. You’ll be reporting to the Manager, Data Engineering (Core).

What You Will Do

• Proactive Business Context & Integration: Partner directly with cross-functional executive stakeholders to translate evolving business strategy into robust analytics architecture, proactively embedding rich context, governance, and self-service capabilities across the data layer.
• Dimensional Modeling Leadership: Architect, document, and maintain complex Kimball dimensional models, standardized metric definitions, enterprise warehouse transformation logic, and scalable BI datasets tailored for high-stakes decision-making.
• Semantic Layer and AI Architecture: Design, build, and govern production-grade data models that power the enterprise semantic layer, enabling seamless AI/ML integrations and business-wide self-service analytics.
• Observability, Quality & Standards: Establish and enforce best-in-class data quality, automated testing, lineage tracking, cost optimization, and proactive alerting frameworks across all dbt transformation pipelines.
• Process Optimization & Scaling: Evaluate and refactor legacy business logic and fragmented query architectures, standardizing disparate analyst workflows into modular, highly scalable semantic pipelines.
• Governance, Security & Mentorship: Drive enterprise data governance, metric standardization, and secure access controls, establishing single-source-of-truth definitions while mentoring junior and mid-level engineers in technical best practices.

Within 1 Month

• Immerse yourself in the company culture, get to know your team, and establish relationships with key business and technical stakeholders.
• Deep dive into our data platform architecture, transformation pipelines, semantic models, and core business metric definitions.
• Participate actively in team ceremonies and begin reviewing current architecture, dbt models, and system performance.
• Deliver on initial high-impact analytics engineering user stories, demonstrating quick value within our existing pipeline frameworks.

Within 3 Months

• Take ownership of key analytics engineering initiatives to expand our semantic layer, support complex reporting needs, and shape our technical roadmap.
• Drive root-cause analysis for complex data logic issues, performance bottlenecks, and modeling edge cases.
• Identify structural gaps in data transformation processes and propose scalable architectural enhancements to the team.

Within 6 Months

• Lead critical workstreams in building our next-generation analytics platform, including advanced dbt models, semantic layer abstractions, and enterprise BI frameworks.
• Independently own key domain models and serve as a technical reference point for complex analytics engineering projects.
• Drive the evolution of business metric definitions and data governance standards across cross-functional domains.

Within 12 Months

• Deliver foundational contributions to our overall analytics platform strategy, significantly advancing our semantic layer capability and company-wide decision-making infrastructure.
• Establish yourself as a recognized subject matter expert and technical leader within analytics engineering, elevating team standards and mentoring fellow engineers.

Qualifications

• Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related field (or equivalent practical experience).
• 6+ years of experience in analytics engineering, data engineering, or enterprise data modeling.
• Proven track record of architecting, deploying, and maintaining mission-critical, production-grade data transformation workflows and enterprise semantic models.
• Expertise in applying dimensional data modeling techniques (Star Schema, Kimball methodology) and leveraging dbt and advanced semantic layer tools to standardize complex metric logic across multi-functional organizations.
• Extensive experience modeling complex business domains (e.g., Salesforce, Google Analytics, ad platforms), product usage metrics, and unstructured/semi-structured data (JSON) for enterprise BI consumption.
• Strong software engineering background applied to data, including advanced git workflows, continuous integration/continuous deployment (CI/CD), modular architecture design, and comprehensive code documentation.
• Advanced proficiency in SQL, dbt (including macros, packages, and performance tuning), Snowflake, Python, Git, and modern BI platforms (e.g., Looker, Tableau).
• Deep expertise with Google Analytics, marketing/ad/social media platforms, CRM/Salesforce systems, and designing custom semantic metrics at scale.
• Comprehensive understanding of modern data stack architecture, transformation strategies, semantic layer implementation, automated testing, governance, and BI integrations.
• Self-directed technical leader, strategic problem solver, highly attentive to detail, exceptional communicator, and driven by metric standardization, performance optimization, and rigorous technical documentation.

Interview Process

• Phone Screen with Talent Acquisition Partner - 30 Minutes
• Video Interview with Hiring Manager - 30 Minutes
• Team Interview - 30 Minutes
• Technical Interview - 90 Minutes