Senior Business Intelligence Engineer- AI-Driven Analytics

SambaSafety• US,

2d•$100,000 - $130,000•Hybrid

About The Position

As a Senior Business Intelligence Analyst on SambaSafety’s Enterprise Data Team, you will sit at the intersection of traditional BI and modern AI-driven analytics. You’ll build the dashboards, semantic layers, and AI-ready data foundations that let business users — and increasingly, AI agents — get trustworthy answers fast. This role is for someone who wants to actively use AI tools in their daily development workflow (not just report on AI as a topic), while applying rigorous data quality and governance discipline to make sure AI-generated insights are accurate, explainable, and trusted.

Requirements

BS in Computer Science, Software Engineering, or a related discipline, or relevant work experience; exposure to the software development lifecycle (SDLC) is a plus
5+ years working in data-related roles, 2+ years involved in company(s) Business Intelligence processes
Expert-level SQL across different RDBMS technologies, plus working knowledge of Python for data analysis and automation
Experience with the Modern Data Stack — Snowflake, Power BI, Looker, and ETL tools; exposure to other Business Intelligence platforms is a plus
Hands-on experience with AI-assisted development tools (e.g., Claude Code, GitHub Copilot, ChatGPT) in a real development workflow — not just casual use
Experience with or strong interest in LLM-powered analytics: natural-language-to-SQL, RAG-based query agents, or Semantic Views
Comfortable acting as the business/semantic-modeling voice in conversations with data engineering — translating business definitions into Semantic View design guidance rather than writing the underlying pipeline code
Working understanding of prompt engineering and how to validate/QA AI-generated outputs before they reach business users
Experience defining and applying data quality frameworks — profiling, validation rules, monitoring — to ensure trusted, decision-ready data
Familiarity with applied ML concepts (forecasting, anomaly detection, clustering) — you don’t need to build models, but you should be comfortable using and explaining their outputs
Experience working with C-suite, product teams, and business users to understand how to optimally deliver insights within their operational workflows and decision-making processes

Nice To Haves

Experience with data governance/catalog tools such as OpenMetadata or Atlan is a plus
Experience with NO-SQL databases and large-scale data is a plus
Knowledge of open-source reporting tools like Redash is a plus

Responsibilities

Design and build AI-augmented dashboards and self-service analytics experiences using Power BI and Looker, including natural-language query interfaces backed by Semantic Views
Use AI-assisted development tools (e.g., Claude Code, GitHub Copilot, ChatGPT) daily to accelerate SQL development, dashboard scaffolding, testing, and documentation
Prototype and evaluate GenAI use cases — automated insight generation, anomaly narratives, AI-written executive summaries — to reduce manual reporting effort
Partner with data engineering and data science to productionize forecasting, anomaly detection, and clustering outputs into BI dashboards business users can act on
Write Python for advanced analysis, automation, and integration with LLM/AI APIs where SQL and point-and-click BI tools aren’t enough
Build and maintain AI-ready metadata (table/column descriptions, glossary terms, certified lineage) in OpenMetadata that powers natural-language-to-SQL agents and other AI query tools
Guide and advise engineering teams on the design and adoption of Semantic Views so business terms map cleanly to underlying tables for both human and AI/agent consumption
Define, monitor, and enforce data quality rules (completeness, accuracy, consistency, timeliness) so AI-generated outputs are built on trusted data, not silently wrong data
Partner with data engineering on metadata cataloging efforts that trace lineage from BI reports back to source Snowflake tables, closing gaps that block AI/analytics reliability
Explore data to discover patterns, relationships, anomalies, and trends, applying structured, hypothesis-driven analysis increasingly augmented by AI/ML-based approaches
Gain a deep understanding of core business processes and align data/AI development with business strategy
Prepare reports, presentations, and dashboards that translate AI-driven and traditional analytics findings into clear, fact-based recommendations for the next steps
Analyze testing results to ensure BI and AI-assisted solutions meet business needs; recommend standards, policies, and procedures for BI tools, systems, and responsible AI use
Present insights to stakeholders up to the C-suite, translating technical AI/analytics outputs into language business decision-makers can act on