• Design and develop data integrations to deliver Enterprise BI Data Marts and BI Semantic Data Layer(s) • Develop modern data engineering pipelines leveraging AI assisted capabilities, such as Databricks GenAI features • Lead multiple projects, provide technical guidance, and support other team members within the DAS organization • Improve data processes, technology infrastructure, and reporting and analytical services

Who we are: Hi, we’re SambaSafety and we offer the industry’s most comprehensive driver monitoring software. Our mission is promoting safer communities by reducing risk through data insights. Companies trust SambaSafety to keep their employees safe on the roads, price and reduce risk, help protect their brand, their bottom line, and our global community. We’ve built an inclusive, supportive, and exceptional culture where every employee is empowered in their role. Don’t take our word for it; we’ve been recognized as a Top Workplace by The Denver Post, Albuquerque Journal, Sacramento Bee, and Built In Colorado. And our employees rate SambaSafety as top-notch, with a rock solid Top Rating on Glassdoor. What You’ll Do: 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. Responsibilities: AI-Augmented Analytics & Development - 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 AI-Ready Data Foundation - 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 Core BI & Business Partnership - 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 What You’ll Need: - 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 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 - 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 - Knowledge of open-source reporting tools like Redash is a plus Benefits and Perks: - Flexible and generous Paid Time Off and Paid Volunteer Days - 401k Employer Match - Generous Healthcare Benefits - Up to 12 weeks paid time off for maternity leave based on tenure - Wellness &Tuition Reimbursement - Flexible Work Arrangements - Lots of SambaSafety swag & SambaSafety Events Our team of talented and committed safety professionals is exceptional. At SambaSafety we strive to foster an inclusive culture that supports, encourages and celebrates a wide array of diversity. We are committed to create a space where all employees can show up as their authentic selves every day, and we work to advance employee equality, diversity and inclusion. SambaSafety provides equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, gender identity, and expression or genetics. Come join us to find out for yourself what all the excitement is about!

• Author and maintain dbt models and PySpark transformation jobs, replacing ad-hoc Snowflake scripts with governed, version-controlled, tested code • Design and implement delivery endpoint configurations as code-customer, delivery target (Snowflake, S3), cadence, cohort filters, incremental and full historical refresh methods • Write production-grade Python and PySpark for data transformation, validation automation, and delivery pipeline components, including customer-specific data models and schema validation logic • Coordinate and execute monthly RWD deliveries across all active channel partners: delivery job execution, manifest generation and validation, tokenization workflows, and QC • Own the channel partner data inquiry queue-triage, investigate, resolve, and communicate on data questions and discrepancies; you are the primary research contact for channel partners • Follow SDLC best practices: author requirements, write test plans, manage releases, and maintain operating documentation in Confluence • Leverage AI tools (including Claude Code) to accelerate development, automate documentation, generate and verify code, and improve operational throughput

• Design, develop, and maintain data pipelines, integrations, and automated data workflows • Build and support data lake, data warehouse, Databricks, and analytics platform solutions • Develop, optimize, and maintain ETL/ELT processes to ingest, transform, and organize data from internal and external systems • Create and maintain analytics-ready datasets, semantic models, and data structures optimized for Power BI consumption, in collaboration with BI Analysts on semantic model design • Develop and maintain data transformation workflows using SQL, Python, Databricks, and other modern data platform tools • Own operational health of the data platform, including monitoring, alerting, incident response, and continuous reliability improvements • Implement data validation, testing, reconciliation, and monitoring processes to ensure data accuracy and integrity • Develop and maintain technical documentation, data dictionaries, lineage documentation, and operational procedures • Collaborate with application developers, BI analysts, and business stakeholders to define data requirements and deliver data solutions • Establish and promote data engineering standards, best practices, and governance processes • Evaluate and recommend improvements to data architecture, tools, processes, and technologies • Administer database and data platform security, including access management, role-based permissions, and auditing of data access • Ensure data solutions comply with privacy, regulatory, and data governance requirements • Manage database and platform operations including backup, recovery, disaster recovery planning, and environment management • Monitor and optimize platform compute utilization and cost, including Databricks cluster configuration and workload efficiency • Implement data retention, archival, and cataloging policies • Participate in solution design, architecture reviews, and strategic data initiatives