Senior Analytics Engineer

Senior Analytics Engineer, Rhino Federated Computing

Rhino Federated Computing (Rhino) solves one of the biggest challenges in AI: seamlessly connecting siloed data through federated computing. The Rhino Federated Computing Platform (Rhino FCP) serves as a data collaboration tech stack, providing computing resources, data preparation, discovery, model development, and monitoring – all in a secure, privacy‑preserving environment. With flexible multi‑cloud and on‑prem architecture, end‑to‑end data management workflows, privacy‑enhancing technologies, and secure deployment of custom code, Rhino is trusted by over 60 leading organizations worldwide, including top hospitals and biopharma companies, and is expanding into financial services, eCommerce, and beyond.

We are seeking a Senior Analytics Engineer to architect, build, and evolve Rhino’s core analytics data infrastructure. This role focuses on data modeling, metrics standardization, data quality and governance, semantic layer design, and building reusable analytics foundations that power decision‑making across the company.

You will own the creation of Rhino’s analytics data models, governed metrics, documentation, and insights generation layer, ensuring that teams across Sales, Customer Solutions, Product, and Business Operations can rely on consistent, trusted, and high‑quality data. This is a high‑impact role combining strong technical rigor with a product‑oriented mindset for enabling data consumers.

• Architect and maintain Rhino’s central analytics data warehouse models, ensuring they are scalable, performant, well‑documented, and aligned with business logic.
• Build and own a consistent metrics layer (semantic layer / metrics catalog) to standardize KPIs across the company and reduce ambiguity in reporting.
• Develop governed, reusable fact and dimension models supporting product usage analytics, platform health analytics, and operational analytics.
• Design and maintain robust Python/SQL pipelines to ingest, clean, transform, and integrate data from sources such as Hub Spot, Jira Service Desk, platform logs/metrics, internal databases, and third‑party systems.
• Build CI/CD‑ready analytics pipelines with monitoring, tests, and data quality alerts.
• Partner closely with Engineering to align data definitions, schemas, and event instrumentation.
• Build and maintain the insights/semantic layer that powers dashboards, self‑service exploration, and automated insights.
• Create intuitive self‑service tools (Looker dashboards, data marts) that help teams easily navigate trusted data.
• Document models and metrics clearly to support cross‑team accessibility and adoption.
• Work with Commercial, Customer Solutions, Delivery, Product, and Operations leadership to translate business questions into data models and metric definitions.
• Serve as the analytics “product manager” for the analytics stack—gather requirements, prioritize improvements, and ensure alignment with strategic needs.
• Close the loop with engineering and product teams to ensure event data, platform telemetry, and operational data are modeled accurately and consistently.
• Leverage LLMs and generative AI to build question‑answering capabilities atop structured data models.
• Build structured embedding‑friendly data layers that support semantic search and automated insights.
• Explore opportunities to accelerate analysis through AI‑assisted workflows and tool chains.

• 5+ years in Analytics Engineering, Data Engineering, Business Intelligence, or related roles, focusing on building data models and governed metrics.
• Expert SQL and Python skills, including production‑grade data transformations and pipelines.
• Strong experience with enterprise data warehouses (Big Query preferred), analytics modeling tools and BI platforms (Looker preferred), and workflow orchestration tools (Airflow or similar).
• Demonstrated ability to design clean, scalable, well‑documented data models and metrics.
• Deep attention to detail regarding data accuracy, definitions, lineage, and quality.
• Strong cross‑functional collaboration and ability to translate business logic into technical data structures.

• Experience building or scaling analytics engineering foundations in a startup environment.
• Familiarity with GTM analytics, product usage analytics, and operational KPIs.
• Experience with Big Query, Looker, Vertex AI, Grafana, Streamlit, or similar modern analytics infrastructure.
• Prior experience with applying LLMs to analytics workflows (semantic layers, insights automation, data Q&A systems).

Hybrid – 3 days onsite in Boston, MA.

Mid‑Senior level

Full‑time

Information Technology

Software Development