Sr Analytics Engineer

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

Education and Experience Requirements

Bachelor’s degree in information systems, Computer Science, Data Analytics, Business, or a related field (or equivalent practical experience)

7-10+ years of experience in analytics, business intelligence, or data modeling roles

Demonstrated experience leading the translation of complex business requirements into enterprise analytical solutions

Proven experience architecting and maintaining semantic data models and analytical solutions at scale

Experience working with modern data platforms (e.g., cloud-based data warehouses, lakehouses, or hybrid architectures)

Strong familiarity with SQL and/or data querying languages is required

Experience mentoring or leading technical team members

Proven track record driving analytics adoption and establishing standards across an organization

Experience with AI-enabled analytics capabilities or data-driven automation preferred

Experience with Microsoft Power BI, Microsoft Fabric, and Azure Synapse Analytics strongly preferred

Knowledge, Skills, and Abilities

Data Modeling & Analytics Expertise: Deep expertise in data modeling principles (e.g., granularity, metric definition, dimensional design) and ability to architect enterprise-grade, scalable, reusable semantic models. Ability to evaluate and recommend modeling approaches and tools.

Business Acumen & Strategic Problem Solving: Ability to translate complex, ambiguous business needs into actionable analytical solutions, think critically at a strategic level, and identify gaps, inconsistencies, and edge cases across multiple business domains.

Technical Proficiency: Deep experience with modern analytics tools and data platforms, with required proficiency in the Microsoft ecosystem including Microsoft Power BI, Microsoft Fabric, and Azure Synapse Analytics, along with advanced data querying, transformation, and performance optimization skills.

AI & Data Literacy: Strong understanding of AI-enabled analytics (e.g., natural language querying, automated insights, generative AI copilots) and ability to evaluate, govern, and validate outputs for accuracy, relevance, and enterprise alignment.

Communication & Executive Stakeholder Engagement: Ability to clearly communicate insights and technical concepts to executive and diverse audiences, facilitate strategic conversations, and influence decision-making through data.

Visualization & User Experience: Advanced knowledge of data visualization best practices to design and review intuitive, user-friendly, and accessible analytical experiences.

Leadership, Governance & Delivery: Demonstrated ability to lead cross-functionally, mentor team members, manage complex priorities, and ensure data quality, consistency, governance, and standards adherence across the analytics practice.

Skill Requirements

Typing/computer keyboard

Utilize computer software (specified above)

Retrieve and compile information

Verify data and information

Organize and prioritize information/tasks

Verbal communication

Written communication

Public speaking/group presentations

Investigate, evaluate, recommend action

Leadership and supervisory, managing people.

Basic mathematical concepts (e.g. add, subtract)

Abstract mathematical concepts (interpolation, inference, frequency, reliability, formulas, equations, statistics)

Advanced mathematical concepts (fractions, decimals, ratios, percentages, graphs)

Physical Requirements

Sitting for extended periods

Extended periods viewing computer screen

Reading

Speaking

Hear/Listen

Maintain regular, punctual attendance

Bending/Stooping

Reaching/Grasping

Writing

Hazards

Normal office environment

KLS Martin is a drug free employer