Overview

At Intuit, we power prosperity around the world and invest in DATA AI and HI to create a done for you experience for our customers.

We are looking for a highly skilled Data Engineer . to build and maintain robust, scalable data pipelines and data marts acting as the connective tissue for intelligence insights generation that serves executive stakeholders , internal Intuit customers and 3rd party

The Fintech AI & Data group is looking for a staff Data Engineer to work closely with analysts, data scientists, and software developers and strengthen Fintech by building data capabilities and AI transformation.

Responsibilities

• Gather data needs from internal customers like product and analysts, and translate those requirements into a working database and analytic software.
• Design, build, and maintain scalable, reliable batch and real time data pipelines, data marts and warehouse supporting executive dashboards, operational analytics, and internal customer use cases
• Ensure high data quality, observability, reliability, and governance across all data assets
• Optimize data models for performance, cost-efficiency, and scalability
• Develop data-centric software using leading-edge big data technologies.
• Build data capabilities that enable automated agentic insights and decision intelligence
• Develop reusable data services and APIs that power AI-driven workflows
• Evolve our data architecture into an AI-native data layer designed to power LLMs, AI agents, and intelligent applications
• Collaborate with analytics, product, and AI teams to translate business needs into scalable data solutions
• Influence the software architecture and working procedures for building data and analytics
• Work bBe the go-to person for anything and everything regarding understanding the data - exploration, pipelines, analytics, etc. and work both independently and as part of a team
  
  

How you’ll succeed

• Have an impact on satisfying customers and reducing financial fraud
• Help build the team by hiring the best talent
• Contribute to experiments and research on how to enhance our capabilities
• Learn new technologies and methodologies
• Collaborate with other data engineers, analysts, data scientists and developers
• Be proactive with a self-starter attitude
• Be a good listener, while also having strong opinions on what is right
• Be fun to be around :)
  
  

Qualifications

• Bachelor’s degree in Information Systems, Computer Science or similar
• Extensive experience dealing directly with internal customers regarding their data needs
• Excellent knowledge of SQL in a large-scale data warehouse or data lakehouse environment such as Spark, Databricks, Presto/Athena/Trino
• Experience in designing, building and maintaining highly scalable, robust & fault-tolerant complex data processing pipelines from the ground up (ETL, DB schemas)
• Experience with stream processing or near real-time data ingestion
• Experience working in cloud environment, preferably AWS (EC2, S3 EMR elastic map)
• Excellent knowledge of database / dimensional modeling / data integration tools
• Experience writing scripts with languages like Python, and shell scripts in a Linux environment
• Can-do attitude, hands-on approach, passionate about data
  
  

Preferred :

• Some knowledge of Data Science/Machine Learning
• Knowledge/Experience with Scala, Java
• Knowledge of data visualization tools like Tableau or Qlik Sense
• Some knowledge of graph databases
• Some experience in Fintech industry, Cyber Security
• Working with AI tools and leveraging AI into product development
  
  

Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.