Senior Analytics Engineer - Business Intelligence

Design, build, and own the data models that serve as the single source of truth for BI reporting — structuring tables, views, and semantic layers so metrics are defined once and used consistently across all dashboards and tools.

Design, build, and maintain ETL/ELT pipelines that feed those models, ensuring data is accurate, timely, and reliable at scale.

Partner with data producers and business stakeholders to translate reporting and decision-making needs into durable data models and pipelines, rather than one-off dashboard logic.

Define, instrument, and maintain business KPIs from scratch using SQL and Python, and ensure definitions stay consistent across teams and dashboards.

Build and enforce data quality checks, validation processes, and documentation so stakeholders can trust the numbers behind leadership- and board-level reporting.

Create and maintain actionable, user-friendly dashboards and visualizations (Tableau, Power BI, or similar) to provide valuable insight.

Support and help evolve leadership-critical reporting deliverables, ensuring governed, well-documented data models rather than ad hoc queries.

Help evolve the team’s data platform and architecture (e.g., improving legacy pipelines), balancing pragmatism with long-term maintainability.

Work with IT and data partners to design secure, sustainable, and scalable paths for data access from source systems into BI-ready models.

Mentor team members across data engineering, data analysis, data validation, and BI reporting — through code and model reviews, pairing, documentation, and by setting reusable standards and best practices.

Cultivate data literacy across the organization through documentation, training, and clear communication of how and why data is modeled the way it is.

Answer complex business questions via ad hoc SQL or Python analysis when a question does not yet warrant a permanent model or dashboard.

Leverage AI-assisted development tools (e.g., GitHub Copilot, Cursor, ChatGPT/Claude) to accelerate SQL, Python, and documentation work, applying sound engineering judgment to review, validate, and take ownership of any AI-generated output before it reaches production.

Evaluate and, where it adds value, implement AI/LLM-powered BI capabilities — such as natural-language querying, semantic layers, and AI-generated summaries or anomaly alerts — to keep our reporting stack modern and to help the team work more efficiently.