Top Senior Data & Analytics Jobs in Austin, TX

Build and maintain analytics data systems and pipelines that capture, transform, and integrate product usage data at scale. Deliver accurate insights and visualizations to inform product decisions, collaborate cross-functionally, and apply AI tools to improve analytics and workflows.

Partner with commercial and delivery teams to build scalable reporting, KPIs, dashboards and forecasts. Analyse sales, pre-sales and delivery performance to reduce time-to-revenue, improve deal velocity, and provide data-driven recommendations to drive commercial growth.

Analyze go-to-market performance across Sales, Marketing, and Client Success, including pipeline, funnel conversion, campaigns, retention, and revenue drivers. Build and maintain dbt models and data marts using Salesforce and modern data-stack tools. Own data quality, documentation, BI-as-code contributions, and AI-enabled analytics workflows. Partner with stakeholders to turn ambiguous business questions into actionable recommendations and deliver results in agile two-week sprints.

Build and maintain scalable GTM data pipelines, marts, semantic models, and dashboards supporting headcount planning, quota attainment, pipeline, and sales effectiveness. Partner with GTM analysts and stakeholders to establish reliable metrics, enable data tools, document systems, and develop AI-powered natural-language data querying capabilities. Translate complex data concepts for executives and engineers while ensuring high-quality, maintainable data products.

Build and scale data pipelines, APIs, integrations, and data models supporting CRM personalization, segmentation, audience activation, and campaign measurement. Ensure pipeline reliability through monitoring, alerting, and logging; automate workflows; document systems; participate in architecture and code reviews; and mentor junior engineers. Collaborate with data engineering, data science, and CRM teams to improve personalized email, push, and SMS engagement for millions of users.

Designs and maintains scalable data infrastructure, ETL/ELT pipelines, canonical datasets, and AI context layers. The role improves data quality and alignment across sales and finance systems, investigates data inconsistencies, leads requirements through UAT and implementation, supports analytics self-service, and partners with cross-functional teams to deliver process improvements and reliable business data.

Lead the strategy and governance for GTM analytics across pre-sales, post-sales, solutions engineering, forward-deployed engineering, and professional services. Establish trusted source-of-truth metrics, data models, semantic layers, self-service analytics, and AI-assisted querying. Partner with Data, Finance, Sales, and Services leaders to resolve metric definitions, improve data access, consolidate reporting, and guide cross-functional programs without direct authority.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.

Designs, builds, and operates scalable data pipelines, services, and products supporting clickstream and behavioral data. Responsibilities include data modeling, API and system design, automated quality frameworks, observability, governance, reliability, security, and performance optimization. The role collaborates with engineering, product, analytics, and domain teams, translates complex requirements into production solutions, and applies AI/ML-enabled tools and workflows. It also contributes to technical decisions, data-driven improvements, and operational excellence.

Lead and grow an enterprise data engineering team while owning the Snowflake and Airflow platform roadmap. Build reliable, governed data products and modernize pipelines using AI-assisted and agentic workflows. Establish standards for quality, observability, lineage, cost efficiency, incident response, and documentation while partnering with Analytics, Data Science, Product, and business stakeholders.