Top Data & Analytics Jobs in Phoenix, AZ

Owns AML transaction-monitoring models across their lifecycle, including performance monitoring, implementation testing, documentation, revalidation support, issue remediation, and model governance. Analyzes transaction data, model outputs, data quality, thresholds, segmentation, and risk factors using quantitative methods and programming. Partners with technology, data, investigations, Model Risk Management, audit, and leadership to resolve findings and communicate model risks.

Design and develop data pipelines, integrations, transformations, and infrastructure across AWS, Snowflake, Airflow, and MySQL. Troubleshoot warehouse issues, validate data quality, improve pipeline reliability, and translate business needs into data engineering solutions. Interpret technical findings for client recommendations, manage workstreams, review junior team members’ work, and provide technical guidance across managed services engagements.

Designs, builds, optimizes, and supports scalable AWS-based data infrastructure, pipelines, data models, and ETL/ELT processes using Python, SQL, PySpark, Databricks, and Spark. The role supports analytics, reporting, and business intelligence through production monitoring, troubleshooting, testing, CI/CD, documentation, and cross-functional collaboration with BI, Product, Engineering, Data, and client-facing teams.

Provide accurate valuations and risk assessments for single-family homes using internal tools and pricing model outputs. Incorporate local market knowledge, collaborate with sales, homes, data science and engineering to improve pricing operations and ensure competitive, consistent offers.

Leads data governance and management activities, including data stewardship, data quality monitoring, risk and incident remediation, data lineage, controls, third-party compliance, and roadmap development. Partners with business, technology, and product teams to implement governance strategies, address data risks, and improve data consistency and usability across the organization.

Supports customer operations through data collection, analysis, reporting, presentations, and operational metric tracking. The intern translates data into actionable insights, communicates findings to internal stakeholders and potentially customers, learns internal systems and processes, and collaborates cross-functionally to improve customer operations and business decisions.

Reviews annotated sensor and map data for quality, identifies recurring issues, and recommends solutions. The role prepares reports, maintains annotation policies, develops training documentation, performs data annotation, tests internal tools, reports bugs, and creates visual assets. It also collaborates with stakeholders, partners, annotators, project managers, and engineers while supporting on-road operations and evolving program priorities.

Leads enterprise data management strategy, data governance, quality frameworks, master data management, operational oversight, and a team of data analysts. Oversees data integrity, metadata, lineage, controls, incident remediation, vendor coordination, reporting, and domain data product health. Partners with business, product, engineering, and governance stakeholders to improve sales, marketing, and CRM data trustworthiness while managing major initiatives, risks, dependencies, and regulatory requirements.

Leads enterprise strategy and analytics initiatives, translating complex business challenges into actionable insights and executive recommendations. Oversees reporting frameworks, forecasting, dashboards, data quality, automation, and AI adoption while partnering with product, technology, operations, and data teams. Manages and develops an analytics team, drives measurable business value, and supports operational efficiency, customer outcomes, profitability, and continuous improvement.

Monitors and analyzes station baggage data, tracer records, runner route playbacks, scanner data, and belt information. Creates visualizations and reports, identifies data trends and performance issues, proposes model adjustments, and presents insights supporting Customer Operations leadership, station improvement, accountability, and decision-making. Collaborates with teams to improve processes and contributes to new analytical tools and functionality.