Staff Data Engineer - Cloud Data Platforms & AI

Intuit; Mountain View (CA); On-site; USD 203,000 - 274,000; Full time

Job summary

Intuit is seeking a Staff Software Engineer in Mountain View, CA to lead data engineering initiatives within the Consumer Group. You will be a technical leader across data frameworks, ingestion pipelines, and tools, working in a cross-functional team with product managers, designers, and data scientists.

You will spend most of your time hands-on writing Java/Scala, Python, and Spark-based data pipelines, with a focus on scalable architectures across AWS/Azure/GCP.

Qualifications

• BS or MS in Computer Science, Data Engineering, or a related field.

Responsibilities

• 70-85% hands-on development in all phases of the software life cycle.
• Rapidly fix bugs and solve problems, and troubleshoot issues, defining ongoing solutions.
• Lead, estimate work for initiatives, create technical design specifications and implement data models.
• Raise team knowledge and engagement through collaborative technical sessions.
• Mentor engineers and develop a strong talent pipeline.
• Guide engineers to elevate experimentation and growth capabilities across the org.
• Understand customer behavior and business levers to drive measurable impact.
• Apply end-to-end system design with operational excellence, security, reliability, performance, and cost optimization.

Skills

Java/Spring

Python

Data engineering

GenAI/AI/ML

Spark

Cloud proficiency

Linux

SQL

Education

BS or MS in Computer Science or Data Engineering

Tools

Docker

Spark

Kubernetes

Jupyter Notebook

Databricks Notebooks

SageMaker

AWS

GCP

Azure

Job description

Intuit is seeking a Staff Software Engineer in Mountain View, CA to lead data engineering initiatives within the Consumer Group. You will be a technical leader across data frameworks, ingestion pipelines, and tools, working in a cross-functional team with product managers, designers, and data scientists.

You will spend most of your time hands-on writing Java/Scala, Python, and Spark-based data pipelines, with a focus on scalable architectures across AWS/Azure/GCP.