Data Platform Engineer for AI & Analytics

Intuit; Toledo (OH); On-site; USD 189,000 - 274,000; Full time

Job summary

Intuit is seeking a Senior Data Platform Engineer to architect scalable, fault-tolerant big-data platforms supporting high-velocity analytics and GenAI initiatives. The role partners with Product, Engineering, and Central Data Lake teams to ensure extensibility and alignment with evolving requirements.

You will drive data normalization, governance, lineage, and discoverability while shaping end-to-end data pipelines and analytics capabilities across business units.

Qualifications

• 8+ years of experience in product analytics, web analytics or similar data analytics domains.
• Advanced proficiency in SQL, big data technologies and BI tools.
• Strong programming skills in Python or Java/Scala.
• Experience in engineering data pipelines and workflow orchestration.
• Understanding of AI-native architectures and GenAI platforms.

Responsibilities

• Architect, design, and build fault-tolerant, scalable big-data platforms.
• Collaborate cross-functionally to align with requirements and future needs.
• Develop architectures for data normalization, lineage, governance, and discoverability.
• Collaborate on datasets for analytics including GenAI and ML.
• Execute code reviews, promote best practices, and CI/CD processes.
• Mentor junior team members and foster technical excellence.

Skills

SQL

Big data tech

Programming: Python

Programming: Java/Scala

Data pipelines

AI/GenAI awareness

Business acumen

Data storytelling

Education

Bachelor’s degree in Engineering/Data Science/CS/Math

Master’s degree preferred

Tools

Redshift

Spark

Hive

BigQuery

Tableau

Qlik

Dash

Job description

Intuit is seeking a Senior Data Platform Engineer to architect scalable, fault-tolerant big-data platforms supporting high-velocity analytics and GenAI initiatives. The role partners with Product, Engineering, and Central Data Lake teams to ensure extensibility and alignment with evolving requirements.

You will drive data normalization, governance, lineage, and discoverability while shaping end-to-end data pipelines and analytics capabilities across business units.