Sr Analytics Engineer (34453)

KLS MARTIN LP • Jacksonville, FL

1d • Onsite

About The Position

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.

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
• 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.
• 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.
• 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.
• 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.
• Ability to clearly communicate insights and technical concepts to executive and diverse audiences, facilitate strategic conversations, and influence decision-making through data.
• Advanced knowledge of data visualization best practices to design and review intuitive, user-friendly, and accessible analytical experiences.
• 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.
• 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)
• Sitting for extended periods
• Extended periods viewing computer screen
• Reading
• Speaking
• Hear/Listen
• Maintain regular, punctual attendance
• Bending/Stooping
• Reaching/Grasping
• Writing

Nice To Haves

• Experience with AI-enabled analytics capabilities or data-driven automation preferred
• Experience with Microsoft Power BI, Microsoft Fabric, and Azure Synapse Analytics strongly preferred

Responsibilities

• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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

Benefits

• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior