Top Data & Analytics Jobs in Boise, ID

Loads, validates, reconciles, and analyzes client investment data using Excel and SQL. Identifies discrepancies, resolves data issues, and improves repeatable onboarding and migration workflows. Collaborates with development teams to enhance automation, accuracy, and delivery speed. Applies accounting, financial markets, securities, normalization, reporting, and reconciliation knowledge while coordinating complex activities in an agile environment.

Supports marketing measurement and analytics by collecting, validating, and analyzing data from digital platforms, CRM systems, web analytics, surveys, and internal systems. Develops Power BI dashboards, scorecards, executive reports, visualizations, and KPI summaries. Identifies trends, risks, opportunities, and data quality issues, then provides actionable recommendations. Explores AI-enabled analytics and automation to improve reporting efficiency and insight generation.

Maintain and extend Snowflake and dbt marketing data models, dashboards, semantic models, KPI frameworks, and AI-agent validation processes. Investigate data quality issues, document metric definitions, assess downstream impacts of marketing technology changes, and translate stakeholder reporting needs into durable self-service data structures. The role requires collaboration across marketing, sales, IT, and finance to ensure trusted data and accurate AI-generated insights.

Lead programmatic analytics for clients and internal teams by analyzing campaign and model performance, designing tests, developing measurement solutions, recommending optimizations, and presenting actionable insights. Partner with Sales, Customer Success, Product, Engineering, and clients to align marketing strategies, reporting requirements, and optimization efforts with business goals.

Leads the strategy, architecture, implementation, and evolution of enterprise analytics platforms. Responsibilities include designing scalable data platforms, integrating digital and enterprise data, establishing governance and security standards, optimizing analytics and BI capabilities, evaluating cloud technologies, managing platform performance and risks, and mentoring technical teams. The role also requires close collaboration with executives, clients, Product, Engineering, Data Science, Marketing, and business stakeholders.

Lead and scale Runpod’s analytics and business intelligence function, managing analysts and BI practitioners while setting the analytics roadmap. Own core company metrics, consumption and marketplace analytics, self-service reporting, experimentation practices, and executive and board-level insights. Partner with Product, Finance, Engineering, Security, Supply, and Go-to-Market leaders to improve data quality, modeling, decision-making, and strategic outcomes.

Build and own end-to-end data products, including pipelines, transformations, semantic models, dashboards, metrics, and AI-enabled workflows. Partner with business and technical stakeholders to turn ambiguous questions into trusted, maintainable solutions. Establish data quality testing, documentation, monitoring, and governance while improving architecture and automation. Use Snowflake, dbt, Power BI, and related technologies to support analytics across Marketing, Product, Finance, and Engineering.

Architect scalable data models and ELT pipelines using modern data lake technologies. Build self-service analytics products, establish enterprise data governance and observability standards, and lead data architecture across engineering teams. Partner with data scientists, product engineers, and business stakeholders to improve data literacy, identify root causes, and connect insights to business outcomes. Develop AI-enabled data tools and frameworks while providing technical leadership, mentorship, and direction for analytics engineering practices.

Design and maintain analytical data models, transformation frameworks, data-quality checks, semantic definitions, and metric consistency across analytics and customer-facing products. Partner with engineering, product, and analytics teams to document data, improve lineage, and enable trustworthy self-service data usage. The role requires advanced SQL, analytics engineering, dimensional modeling, testing, and collaborative translation of business concepts into precise data definitions.

Lead the architecture, development, and scaling of data pipelines and infrastructure for a customer-facing Data Lake platform. Build ETL and ingestion systems using GCP, Debezium, Pub/Sub, Dataflow, BigQuery, Python, Golang, and SQL. Establish engineering standards, deliver strategic projects, mentor engineers, influence cross-functional technical decisions, optimize data services, and shape the platform roadmap.