Principal Technical Data Analyst

    • Intuit
  • Mountain View, CA
  • Posted 60+ days ago | Updated 6 hours ago


On Site
Full Time


Financial technology
Service operations
Artificial intelligence
Advanced analytics
Computer science
Data Science
Information Technology
Data warehouse
Data integration
Programming languages
Business intelligence
Qlik Sense
Data Visualization
Analytical skill
Problem solving
Business requirements
Project management
Data architecture
Apache Velocity
Product development
Data marts
Big data
Data engineering
Machine Learning (ML)
Generative Artificial Intelligence (AI)
Business data
Data modeling
Data governance
Data quality
Meta-data management

Job Details

Company Overview

Intuit is the global financial technology platform that powers prosperity for the people and communities we serve. With approximately 100 million customers worldwide using products such as TurboTax, Credit Karma, QuickBooks, and Mailchimp, we believe that everyone should have the opportunity to prosper. We never stop working to find new, innovative ways to make that possible.

Job Overview

We are seeking a highly skilled and experienced Principal Data Architect and Analytics Engineer to join our Service Analytics team. This role is at the center of an exciting journey as Intuit transforms its strategy to become an "AI-driven Expert Platform'', partnering with experts to help solve the most pressing customer problems. Service Analytics provides the foundational insights to enable and support a two-sided marketplace to connect our Customers to our Experts.

The ideal candidate for this role will possess a unique blend of technical expertise in data architecture, engineering, and analytics, with a strong focus on designing scalable Service/Operations data models, integration of AI models in data architectures to create/transform data, optimizing data pipelines, and providing actionable insights through advanced analytics and data visualization techniques.


  • Bachelor's or Master's degree in Computer Science, Data Science, Information Technology, or a related field or related work experience.
  • Minimum of 10 years of experience in a data architect or analytics engineer role, with a proven track record of delivering complex data projects.
  • Strong knowledge of data modeling, data warehousing, and data integration techniques. Expertise in SQL and experience with programming languages (e.g., Python, R).
  • Familiarity with BI tools (e.g., Qlik Sense, Tableau) and data visualization techniques.
  • Excellent analytical and problem-solving skills, with the ability to translate business requirements into technical solutions.
  • Strong communication, collaboration, and project management skills, capable of working effectively with teams across the organization.


  • Owner of Expert Network / Modern Operations data architecture
  • Partner with data engineering and broader analytics teams to build durable and accurate data marts.
  • Build automated and scalable data pipelines that refresh frequently and in a short time frame.
  • Continuously monitor data and system performance, proposing and implementing improvements as needed.
  • Increase the velocity of the broader analytics team by reducing analyst time spent on data discovery, ETL, and data modeling.
  • Provide guidance and thought partnership to business leaders and product development teams on how to best harness available data in support of critical business needs and goals.
  • Work with data engineering and product development teams to identify and instrument new data sources. Ensure there is clean data at source and minimize the use of business logic in data marts.
  • Advise analysts on how to efficiently leverage big data (clickstream, transcripts) to be consumed by ML and Gen AI models. Provide requirements to data engineering to automate these pipelines.
  • Adapt ML models and Gen AI outputs into consumable data models for analysts to leverage
  • Work closely with business stakeholders and business data analysts to understand data requirements and deliver comprehensive analytics solutions.
  • Teach business data analysts data modeling and visualization tools best practices and mentor junior technical data analysts in order to build scalable data products for stakeholders.
  • Implement best practices for data governance, including data quality, metadata management, and data privacy.
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