Senior Business Intelligence Engineer- AI-Driven Analytics

SambaSafety is a leader in driver monitoring software, dedicated to promoting safer communities through data insights. They are seeking a Senior Business Intelligence Engineer to develop AI-augmented dashboards and analytics, ensuring data quality and governance while collaborating with engineering teams to enhance business intelligence capabilities.

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

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
• 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