Sr Analytics Engineer

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.

• 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.

• Partn…