Top Data Analyst Jobs in Leon

Analyze production drive-thru voice AI data, build SQL reports and dashboards, monitor KPIs, identify conversation error patterns, and improve NLU models, conversation flows, and LLM behavior through machine learning and rule-based techniques.

Build and maintain data models, dashboards, reports, and dbt models supporting cross-functional decision-making. Process large datasets, establish reporting sources of truth, bridge technical and business teams, and document knowledge bases and business definitions for internal AI systems. The role also involves identifying cost-saving opportunities, communicating with stakeholders, and using AI to develop solutions and fix bugs.

Own the growth experimentation program, including test design, power calculations, statistical rigor, readouts, and anytime-valid monitoring. Build a trusted acquisition-to-conversion funnel across paid, organic, and partner channels, incorporating CAC, LTV, and conversion metrics. Extend self-serve experimentation tooling in Lightdash and Python, improve instrumentation with Segment and Flagsmith, automate routine analysis, and influence Growth PM and marketing decisions through clear, statistically sound insights.

Lead product data analyst responsible for marketplace modeling, statistical analysis, reporting, dashboard development, and dbt-based analytics engineering. Partners with product managers and business stakeholders to establish trusted metrics, enable self-service reporting, and influence decisions through actionable insights. Uses advanced SQL, intermediate Python or R, Lightdash, and AI tools. This is an autonomous individual-contributor role focused on the Pro supply side of the marketplace.

Monitor and improve healthcare data file exchanges across clients, vendors, and internal systems. Analyze insurance claims, eligibility, enrollment, and EDI file mappings; troubleshoot data, file movement, and SFTP issues; coordinate EDI testing and implementations; validate data against business requirements; communicate operational status; and improve DataOps workflows, documentation, and processes.

Analyze user behavior, product performance, experiments, and academic research data to guide product decisions and health outcomes. Responsibilities include building Mixpanel dashboards, validating event tracking, querying Snowflake with SQL, developing Python analyses and automation, maintaining data pipelines, creating BI visualizations, conducting user research, performing statistical testing, preparing research manuscripts, and supporting grant-funded projects.

Investigate and resolve streaming catalog, feed, data, and process issues using Python, APIs, logs, and technical workflows. Identify root causes, troubleshoot independently, escalate complex problems with clear diagnoses, and communicate findings to technical teams and clients. Use AI tools and prompting to support analysis, document solutions, manage multiple investigations, and improve recurring workflows while meeting deadlines and SLA requirements.

Early-career Data Engineering Analyst supporting AIG’s Data Office through a rotational program. Responsibilities include developing cloud-based data pipelines, collecting and transforming data, building modern software systems, and supporting analytics and data science use cases. The role emphasizes data quality, security, compliance, collaboration, analytical problem-solving, and professional development through mentoring, masterclasses, and on-the-job experiences.

Serves as a bridge between procurement stakeholders and technical teams by translating business needs into SAP and data-driven solutions. Analyzes procurement processes, identifies optimization opportunities, and delivers insights through analytics and reporting. Collaborates cross-functionally to improve operational excellence and support informed decision-making.

Monitor transactions and customer accounts for suspicious activity, investigate suspected fraud, analyze large datasets with SQL and Python, identify fraud patterns, document findings, and develop prevention rules and controls. The role also requires staying current on emerging fraud threats, using fraud detection tools, and leveraging Claude or equivalent large language models to improve productivity, research, and communication.

Lead a multidisciplinary analytics team across product, retention, AI, chat, and operations. Own analytics roadmaps, KPI frameworks, dashboards, funnel and cohort analysis, retention strategy, conversational metrics, and operational performance measurement. Translate ambiguous business questions into actionable insights, product recommendations, experiments, and measurable outcomes. Partner with Product, Operations, Growth, AI, Customer Experience, and leadership while developing analytical talent and improving data-driven decision-making.

Own data product reliability, release quality, and customer-facing correctness across large-scale, frequently updated datasets. Responsibilities include monitoring pipeline quality, designing aggregate test plans, investigating root causes, managing cross-functional releases, communicating timelines to clients, expanding data coverage, and partnering with Commercial, Data Science, and Engineering. The role requires expert SQL, sound judgment with ambiguous data, and accountability for business outcomes.

Own newsroom analytics supporting editorial, audience, and product decisions. Develop dashboards, automated reporting, measurement frameworks, deep-dive analyses, and A/B tests. Maintain reporting data quality and pipelines in partnership with engineering and product teams. Translate findings for nontechnical stakeholders, strengthen analytical practices, mentor junior analysts, and drive adoption of insights across the organization.

Write SQL queries, build dashboards and recurring reports, support newsroom analyses and A/B testing, troubleshoot data quality issues, and communicate findings to editorial, audience, and product partners. The role also involves maintaining documentation, collaborating with data engineering, and using AI tools to automate analytical workflows.

Gather and document business and data requirements, create functional specifications and source-to-target mappings, and analyze healthcare payer datasets using SQL and Databricks. Define transformation rules with technical teams, support UAT and data validation, document data lineage and dictionaries, identify data quality issues, and facilitate communication between stakeholders and engineering teams to improve reporting and operational efficiency.

Lead a multidisciplinary analytics team supporting product, retention, AI, chat, and operations. Own KPI frameworks, conversion funnels, lifecycle and retention analysis, conversational analytics, dashboards, experimentation, and operational performance measurement. Translate ambiguous business problems into actionable insights, product recommendations, and measurable experiments while partnering with Product, Operations, Growth, Fintech, AI, and leadership stakeholders. Establish the analytical roadmap, develop team talent, and improve data-driven decision-making across the organization.

The Revenue Operations Data Analyst will analyze datasets, optimize dashboards, and develop data pipelines to support revenue operations and strategic decision-making.

Analyzes human-generated data quality across AI programs using SQL, Python, statistical methods, and hands-on annotation review. Investigates quality issues, identifies failure patterns, measures impact, and develops recommendations. Supports quality pipelines, automated validations, rubrics, gold sets, calibration exercises, reporting, and dashboards. Collaborates with quality engineering, operations, delivery, and engineering teams to improve annotation design, training, tooling, and quality controls.

Integrate and validate marketing data from multiple sources, ensure accurate campaign tracking and attribution, analyze paid media performance, support A/B testing, build scalable dashboards and pipelines, and partner with marketing and product teams to define KPIs and recommend optimizations.

The Data Analyst role focuses on leveraging advanced analytics to address strategic business challenges, including designing and implementing analytical solutions.

Builds automated analytics and reporting solutions for FP&A by reverse-engineering Excel processes, querying and reconciling multi-system data, developing dashboards and interactive HTML reports, and exploring AI-assisted workflows. The role performs exploratory and driver-based analysis, improves data pipelines and data marts in partnership with engineering teams, monitors exceptions, translates findings for Finance leaders, and helps establish an AI repository for repeatable FP&A processes.

Design and maintain dashboards and reports, perform deep-dive analyses to identify trends and anomalies, ensure data integrity with engineers and data scientists, automate reporting, and translate complex datasets into clear insights for non-technical stakeholders.

Collaborates with clients to define business needs, designs data and business architectures, identifies data and AI opportunities, and translates requirements into technical specifications. Leads workshops, gap analyses, stakeholder alignment, governance improvements, and solution implementation. Develops delivery frameworks and accelerators while supporting client relationship growth and quantifying business value and ROI.

Supports the setup, approval, release, and tracking of new part numbers, bills of materials, and engineering change orders. Reviews changes for compliance with engineering and process standards, assigns tasks, coordinates cross-functional troubleshooting, escalates delays, and assists with testing new tools and processes. The role requires collaboration across business units and locations, knowledge of Oracle procedures, and familiarity with PLM and BOM change management.

Partner with Marketing, Sales, Finance, Product and other teams to turn business questions into actionable analysis. Own end-to-end analytics: build datasets and dashboards (dbt, LookML), maintain metric definitions and caveats, use and curate agent tooling for trustworthy answers, and contribute small data platform and pipeline improvements.