Sr Analytics Engineer (34453)

Kls Martin Lp; Jacksonville (FL); On-site; USD 90,000 - 130,000; Full time

Job summary

Kls Martin Lp is seeking a Senior Analytics Engineer to lead complex analytical solutions and serve as a resource for other engineers. This role involves managing the entire lifecycle of analytics delivery while collaborating closely with business stakeholders on strategic initiatives.

The ideal candidate will possess strong experience in data modeling and analytics, particularly with Microsoft Power BI and Azure technologies. This position offers an opportunity to innovate and develop AI-enabled capabilities within the organization.

Qualifications

• 7‑10+ years of experience in analytics, business intelligence, or data modeling roles.
• Experience with AI‑enabled analytics capabilities or data‑driven automation preferred.
• Proven experience architecting and maintaining semantic data models and analytical solutions at scale.

Responsibilities

• Lead stakeholder engagement, translating complex business questions into structured analytical requirements.
• Design and build complex, reusable semantic models for high‑priority business processes.
• Lead the development and delivery of complex analytical assets.

Skills

Data Modeling & Analytics Expertise

Business Acumen & Strategic Problem Solving

Technical Proficiency

AI & Data Literacy

Communication & Executive Stakeholder Engagement

Visualization & User Experience

Leadership, Governance & Delivery

Education

Bachelor’s degree in information systems, Computer Science, Data Analytics, Business, or related field

Tools

Microsoft Power BI

Microsoft Fabric

Azure Synapse Analytics

Job description

Job Summary

The Senior Analytics Engineer is a hands‑on technical lead who owns the organization’s most complex analytical solutions and serves as a resource for other Analytics Engineers. This role manages the end‑to‑end lifecycle of analytics delivery—from requirements elicitation and semantic modeling to insight generation and user adoption—while serving as the primary interface with business stakeholders on high‑complexity initiatives. The role also evaluates and integrates AI‑enabled capabilities such as natural language querying, automated insights, and copilots, ensuring that AI‑generated outputs are governed, accurate, 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.

Qualifications

• 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 and ability to architect enterprise‑grade, scalable, reusable semantic models.
• Business Acumen & Strategic Problem Solving: Ability to translate complex, ambiguous business needs into actionable analytical solutions.
• Technical Proficiency: Deep experience with modern analytics tools and data platforms, especially Microsoft Power BI, Microsoft Fabric, and Azure Synapse Analytics.
• AI & Data Literacy: Strong understanding of AI‑enabled analytics and ability to evaluate, govern, and validate outputs for accuracy and alignment.
• Communication & Executive Stakeholder Engagement: Ability to clearly communicate insights and technical concepts to diverse audiences.
• Visualization & User Experience: Advanced knowledge of data visualization best practices to design intuitive, user‑friendly analytical experiences.
• Leadership, Governance & Delivery: Demonstrated ability to lead cross‑functionally, mentor team members, and ensure data quality, consistency, governance, and standard adherence.

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).

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. At this time, we are not able to provide visa sponsorship or support employment authorization for this position. Candidates must be authorized to work in the United States without current or future sponsorship.