Overview
On Site
Full Time
Skills
Network
Product Support
HR Analytics
Leadership
Strategic Planning
Big Data
Apache Velocity
Use Cases
Analytics
Data Science
Data Engineering
Data Architecture
Normalization
Ontologies
Artificial Intelligence
Advanced Analytics
Generative Artificial Intelligence (AI)
Collaboration
Algorithms
Machine Learning (ML)
Unit Testing
Continuous Integration
Continuous Delivery
Performance Testing
Capacity Management
Documentation
Incident Management
Problem Solving
Conflict Resolution
Job Details
Join the Intuit Customer Success team as a Staff Analytics Engineer within our Expert Network. In this role, you will drive the data enablement strategy for our product support and live experiences. You'll be central in optimizing our greatest resource-our people-through innovating, experimenting, learning, pivoting, and scaling.
As a Staff Analytics Engineer, you will join our team of Analytics Engineers, Data Analysts, and Data Scientists to develop tools, define metrics, and build robust Data/AI infrastructure that informs critical business decisions. You will also partner closely with Engineering leadership, Strategy, Planning, & Analysis teams, to shape and execute the overarching Analytics and AI strategy.
Responsibilities
Big Data Platform Development: Architect, design, and build fault-tolerant, scalable big-data platforms that support high-velocity, high-volume data use cases.
Cross-Functional Collaboration: Partner with Analytics, Data Science, and Central Data Engineering teams to ensure the platform meets immediate needs and remains extensible for future capabilities.
Data Architecture: Create and maintain scalable solutions for data normalization, lineage, governance, ontology, and discoverability, ensuring seamless integration across multiple systems and potentially different business units.
Data & AI Enablement: Work with analysts and data scientists to identify, prepare, and maintain datasets required for advanced analytics (GenAI, ML) and actionable customer insights.
Production-Ready ML Solutions: Collaborate with scientists and engineers to design and deploy scalable machine learning solutions, moving algorithms from concept through to production within Intuit's platform. Possess a broad understanding of ML fundamentals, end-to-end pipelines, and common challenges such as model explainability and scalable deployment.
Engineering Best Practices: Conduct code reviews, drive coding best practices, and champion processes for unit testing, CI/CD, performance testing, capacity planning,documentation, monitoring, alerting, and incident response. Lead by example and foster a culture of continuous learning, guiding junior team members on technical excellence and collaborative problem-solving.
As a Staff Analytics Engineer, you will join our team of Analytics Engineers, Data Analysts, and Data Scientists to develop tools, define metrics, and build robust Data/AI infrastructure that informs critical business decisions. You will also partner closely with Engineering leadership, Strategy, Planning, & Analysis teams, to shape and execute the overarching Analytics and AI strategy.
Responsibilities
Big Data Platform Development: Architect, design, and build fault-tolerant, scalable big-data platforms that support high-velocity, high-volume data use cases.
Cross-Functional Collaboration: Partner with Analytics, Data Science, and Central Data Engineering teams to ensure the platform meets immediate needs and remains extensible for future capabilities.
Data Architecture: Create and maintain scalable solutions for data normalization, lineage, governance, ontology, and discoverability, ensuring seamless integration across multiple systems and potentially different business units.
Data & AI Enablement: Work with analysts and data scientists to identify, prepare, and maintain datasets required for advanced analytics (GenAI, ML) and actionable customer insights.
Production-Ready ML Solutions: Collaborate with scientists and engineers to design and deploy scalable machine learning solutions, moving algorithms from concept through to production within Intuit's platform. Possess a broad understanding of ML fundamentals, end-to-end pipelines, and common challenges such as model explainability and scalable deployment.
Engineering Best Practices: Conduct code reviews, drive coding best practices, and champion processes for unit testing, CI/CD, performance testing, capacity planning,documentation, monitoring, alerting, and incident response. Lead by example and foster a culture of continuous learning, guiding junior team members on technical excellence and collaborative problem-solving.
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