Staff Data Engineer - Big Data, Spark & AI Pipelines

Intuit, Inc.; San Diego, Northern (CA, KY); Hybrid; USD 189,000 - 255,000; Full time

Job summary

Intuit, Inc. is seeking a Staff Software Engineer to lead data engineering initiatives within the Consumer Group.

The role focuses on building data frameworks, ingestion pipelines, and tooling using Google Cloud, Spark, Flink, and AWS Big Data services. You will drive end-to-end delivery across one or more scrum teams, mentor engineers, and collaborate with product managers, designers, and data scientists to ideate and validate hypotheses through rapid experimentation.

Qualifications

• BS or MS in Computer Science, Data Engineering, or a related field.
• 8+ years of software engineering experience, with a strong background in data engineering.
• 5+ years of expert experience with SQL, XML, JSON, YAML, strong Python and Linux.
• Experience building and using AI/ML, GenAI, and growth-focused AI/ML applications.

Responsibilities

• 70-85% hands-on development in all phases of the software life cycle.
• Lead technical design specifications and implement data models while coordinating with teams.
• Troubleshoot complex, cross-capability issues and drive timely delivery.
• Mentor engineers, grow team knowledge, and foster inclusive culture.
• Collaborate with product managers, designers, and data scientists to validate hypotheses.
• Promote growth engineering and measure business impact through experiments.

Skills

Java/Spring

Scala for Spark

Python

SQL

Linux

AI/ML

GenAI

Education

BS in Computer Science or related field

MS in Computer Science or related field

Tools

Docker

Spark

Kubernetes

Jupyter Notebook

Databricks Notebooks

SageMaker

AWS

GCP

Azure

Job description

Intuit, Inc. is seeking a Staff Software Engineer to lead data engineering initiatives within the Consumer Group.

The role focuses on building data frameworks, ingestion pipelines, and tooling using Google Cloud, Spark, Flink, and AWS Big Data services. You will drive end-to-end delivery across one or more scrum teams, mentor engineers, and collaborate with product managers, designers, and data scientists to ideate and validate hypotheses through rapid experimentation.