• Collaborate with engineers, product teams and analysts to develop data products that are precise and insightful. • Own the data product lifecycle, including designing tracking plans, developing data models, ELT pipelines, and self-serve data products. • Design and maintain an AI-enabled semantic layer that allows business users to interact with data through natural language using agentic AI tools. • Find creative ways to integrate AI into the Data lifecycle. • Be the data steward, ensuring data quality and consistent metrics.

• Build machine-learning systems that power some of the most advanced logistics intelligence products in the industry • Analyze large, noisy datasets from cameras, OCR, detections, and geospatial pipelines to uncover actionable patterns • Design and evaluate algorithms for truck re-identification, geospatial clustering, equipment classification, OCR text labeling, anomaly detection, and more • Collaborate with engineering and data engineering teams to scale models from prototype to production • Work closely with product teams to deeply understand customer needs and translate them into modeling and analytics initiatives • Apply scientific thinking to continuously test, iterate, and refine approaches as new data becomes available

• Develop and maintain data replication and ELT pipelines feeding the analytics platform • Design and implement dimensional data models using star and snowflake schemas • Build and maintain data transformations that convert operational data into analytics-ready datasets • Implement data quality checks, testing, and validation to ensure accuracy of KPIs and metrics • Create and maintain documentation for data models, business logic, and data lineage • Establish data governance standards for analytics, including naming conventions, access controls, and metric definitions • Partner with BI tool users (Tableau, Looker, Power BI) to design efficient, performant data structures • Mentor junior engineers and analysts on data modeling, SQL optimization, and analytics best practices

• Partner with leadership to drive strategy • Collaborate with product, operations, and go-to-market leaders to identify the most impactful problems to solve and translate ambiguous business questions into structured analytical frameworks • Present insights and recommendations to executives and influence strategic decisions across your business unit • Develop a deep understanding of your domain's challenges, competitive landscape, and growth opportunities • Own key business metrics and KPIs: defining, instrumenting, and evangelizing them across stakeholders • Build scalable data infrastructure • Design, build, and maintain dimensional data models using dbt that serve as the foundation for analytics across your domain • Create self-service dashboards and reporting tools in Looker/Databricks that empower teams to answer their own questions • Establish data quality standards and monitoring to ensure stakeholders can trust the data • Operationalize analytical workflows that scale beyond one-time analyses—building reusable frameworks, automated monitoring, and sustainable data products • Own high-impact analytical initiatives • Depending on your business unit, you might: App: Design and analyze growth experiments, build fraud detection models, optimize conversion funnels, and measure product feature adoption to drive user growth and reduce losses API: Create partner health scorecards, analyze API usage patterns, monitor integration reliability, and identify expansion opportunities to strengthen partnerships and operational visibility Carrier Operations: Develop carrier variance and cost attribution models, track SLA performance, identify margin leakage, and improve operational efficiency to recover costs and drive accountability.