Sr Analytics Engineer (34453)

Who We Are

At KLS Martin, we offer a unique opportunity to contribute to the success of a dynamic and thriving company whose products are used daily across the world to help surgical patients.

The KLS Martin Group is a worldwide leader in creating surgical solutions for the craniomaxillofacial and cardiothoracic fields. Surgical innovation is our passion, and we are constantly working with surgeons to improve surgical care for their patients. Our product portfolio includes titanium and resorbable implants for reconstruction, innovative distraction devices to stimulate bone lengthening, over 4,000 surgical instruments, and other surgical products designed specifically for CMF and cardiothoracic surgeons.

KLS Martin is an innovative leader in the treatment of CMF deformities and trauma cases. We use Individual Patient Solutions (IPS) by using our proprietary IPS products where CT scans are used to custom design implants that are created specifically for that individual patient. This technology allows our surgeons to provide the best-in-class treatment for their patients.

KLS Martin Guiding Principles

• Established, Privately Held Business Group: Responsive to customers, not shareholders. KLS Martin has manufactured medical products since 1896, and we have sold our products in the United States under the KLS name since 1993. We have always been, and always will be, privately owned.
• Patient Focus: We design products with the patient in mind CMF, Thoracic & Hand
• Product to Table: Integrated planning, design, manufacturing and distribution process
• Educational Partner: Our primary focus for support is on education
• Inventory Alliance: Inventory management is critical to patient treatment/outcome
• Surgical Innovation is Our Passion: More than just a tagline

What We Offer

• We provide full-time employees with a competitive benefits package, including paid parental leave
• In-house training and professional development opportunities
• A culture of creativity and innovation by drawing on diverse perspectives and ideas to drive surgical innovation

Job Summary

The Senior Analytics Engineer is a hands-on technical lead who takes direct ownership of the organization's most complex analytical solutions while serving as a resource and sounding board for other Analytics Engineers on less complex work. This role owns the end-to-end lifecycle of analytics delivery - from requirements elicitation and semantic modeling to insight generation and user adoption - and serves as the primary interface with business stakeholders on high-complexity initiatives.

Operating within a small, collaborative team, this role requires deep engagement in direct solutioning alongside a natural inclination to share knowledge and help teammates succeed. The role emphasizes a product-oriented mindset, treating analytics assets as long-lived, evolving products and establishing practical standards the team can apply consistently.

As data platforms increasingly incorporate artificial intelligence, this role drives hands-on evaluation and integration of AI-enabled capabilities (e.g., natural language querying, automated insights, copilots) and ensures that AI-generated outputs are accurate, governed, and aligned with business semantics.

Essential Functions, Duties, and Responsibilities

Business Engagement & Requirements Engineering

• Lead stakeholder engagement, translating complex and ambiguous business questions into structured analytical requirements
• Facilitate and lead workshops to define KPIs, metrics, dimensions, grain, and business rules
• Challenge and refine requirements to align with strategic decision-making objectives
• Establish and enforce documentation standards for definitions, assumptions, and data logic to ensure transparency and consistency across the team
• Serve as escalation point for complex requirements that cross multiple domains or business units

Semantic Modeling & Data Design

• Design and build complex, reusable semantic models for high-priority or technically demanding business processes
• Define and enforce standards for core metrics, ensuring consistency and a single version of truth across all analytical outputs
• Apply and champion sound data modeling principles (e.g., dimensional modeling, normalization vs. denormalization trade-offs)
• Ensure models are optimized for performance, usability, and long-term extensibility
• Evaluate and recommend semantic layer technologies and modeling approaches for the organization

Analytics Development & Delivery

• Lead the development and delivery of complex analytical assets (dashboards, reports, data products, self-service datasets)
• Establish and enforce architectural standards with clear separation between data, semantic, and presentation layers
• Define best practices for data transformation, calculation logic, and visualization design across the team
• Ensure solutions are intuitive, performant, scalable, and aligned with user workflows
• Review and approve analytical deliverables produced by junior team members

AI-Augmented Analytics & Innovation

• Lead evaluation, adoption, and governance of AI-enabled capabilities (e.g., natural language interfaces, automated insights, generative copilots)
• Establish frameworks for validating and governing AI-generated insights, ensuring alignment with enterprise data definitions and quality standards
• Identify and champion opportunities to embed predictive or prescriptive insights into analytics experiences
• Develop organizational readiness for AI-driven analytics through education, documentation, and governance frameworks
• Stay ahead of emerging AI and analytics technologies, making recommendations for strategic adoption

Data Quality, Validation & Governance

• Own the validation of analytical outputs against source systems and business expectations
• Lead resolution of complex data quality issues, including systemic inconsistencies in definitions or logic
• Define and enforce enterprise governance standards for naming, documentation, and metric certification
• Prevent duplication of logic and ensure a "single version of truth" across all analytics assets
• Partner with data governance and compliance teams to implement and audit standards

Stakeholder Communication & Adoption

• Communicate complex insights and technical concepts effectively to executive, technical, and non-technical audiences
• Guide and enable stakeholders in interpreting data and using analytical tools effectively and responsibly
• Drive organizational adoption of analytics solutions through training, documentation, and iterative improvements
• Act as a trusted strategic advisor for data-driven decision-making at senior levels
• Present analytical findings and platform roadmap updates to leadership

Collaboration with Data Engineering Team

• Partner with data engineering to define and prioritize data requirements (e.g., granularity, latency, transformations)
• Provide authoritative feedback on upstream data structures to improve downstream analytics usability
• Align with platform architecture, performance constraints, and data lifecycle management practices
• Drive cross-functional alignment between analytics, engineering, and business teams

Mentorship, Leadership & Continuous Improvement

• Mentor and coach junior Analytics Engineers, fostering growth in data modeling, analytics design, and stakeholder engagement
• Define and document team standards, best practices, and frameworks for analytics development and governance
• Manage analytics solutions as products, including backlog prioritization, iteration, and strategic enhancement
• Continuously evaluate and improve existing assets for performance, usability, and business impact

Participate in hiring, onboarding, and capability development within the analytics function