Top Senior Data & Analytics Jobs in Austin, TX

Lead design and implementation of data architecture for healthcare systems, influence stakeholders, mentor teams, ensure regulatory compliance, enable data integration and transformation, and apply AI techniques to improve healthcare operations and reporting.

Designs, builds, and optimizes enterprise data pipelines, architectures, and applications supporting financial consolidation, planning, forecasting, reporting, and analytics. Develops ETL/ELT workflows, data models, quality checks, dashboards, and integrations across cloud and relational platforms. Manages projects, partners with Finance and technology teams, improves processes, mentors junior staff, contributes to Agile planning, and evaluates emerging AI and data technologies. Rotational after-hours on-call availability may be required.

Lead data engineering initiatives supporting analytics, machine learning, and business intelligence teams. Design complex data warehouse and data lake models, manage large-scale SQL and Python ETL/ELT pipelines, maintain data quality and lifecycle standards, and translate strategic priorities into actionable projects. Provide technical leadership, mentorship, architectural guidance, project planning, and cross-functional collaboration with product, infrastructure, and architecture teams.

Build and operate scalable data pipelines and trustworthy data models supporting Layla’s AI-powered travel products. Own data quality, reliability, observability, experimentation, and model-evaluation foundations. Enable production AI/ML systems through feature and data availability, evaluation datasets, and monitoring. Partner with Product, Analytics, and ML teams to deliver dependable insights, establish engineering best practices, and mentor other engineers.

Build and maintain master data, metadata, repositories, logical data models, and data standards. Assemble complex datasets, improve data processes and infrastructure scalability, and develop industrialized analytical datasets and delivery mechanisms. Use data pipelines to generate insights into customer acquisition, operational efficiency, and business performance metrics. This is a hybrid role requiring three days in the office and two days remotely.

Own operational analytics for Central Engineering, developing KPIs, dashboards, data pipelines, forecasts, scenario models, and executive insights. Analyze headcount, resources, Capex/Opex, budgets, variances, and total cost of ownership to support multi-million-dollar investment decisions. Lead business review cadence, governance, execution tracking, and cross-functional risk resolution while improving reporting processes and operational predictability across engineering sites.

Analyze customer adoption, engagement, health, and churn-risk data. Define consistent metrics, build dashboards and cohort analyses, monitor onboarding and implementation performance, identify friction and success patterns, and translate findings into actionable recommendations for customer-facing teams and leadership. Collaborate with revenue operations, AI engineering, analytics, and customer teams to document metric logic and improve data-driven customer experience operations.

Lead analytics for lead intelligence and revenue optimization: develop forecasting, modeling, and simulation to prioritize lead inventory, recommend allocation rules, build KPIs, and partner cross-functionally to implement rules and drive revenue outcomes.

Lead global Customer Success analytics strategy, architect data and health/adoption models, oversee data pipelines, deliver executive-level storytelling, mentor an analytics team, and drive cross-functional KPI alignment and self-serve BI adoption.

The Senior Revenue Operations Analyst will enhance GTM data quality, integrate 3rd party data into CRM, and solve data issues collaboratively with various teams.