Staff Software Engineer, Experimentation Platform

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

Job summary

Intuit in Oakland seeks a Staff Software Engineer to lead data architecture for the experimentation platform. You will design scalable data models, pipelines, and governance to support thousands of metrics and multiple statistical methods.

You will partner with data scientists, analysts, product managers, and engineers to set the technical roadmap, deliver low-latency analytics on Google Cloud, and mentor junior engineers while driving high-impact, observable systems.

Qualifications

• 10+ years in software engineering focusing on data engineering and data architecture.
• Proficiency in Scala, Python, and SQL.
• Experience building large-scale data pipelines with Spark, Flink, Dataflow, BigQuery, or Airflow/Composer.
• Familiarity with Python libraries for statistical analysis (Statsmodels, SciPy) is a plus.

Responsibilities

• Define Technical Strategy: provide the roadmap and architecture for the platform’s infrastructure.
• Develop Near Real-Time Systems: lead initiatives to build a real-time ecosystem on Google Cloud.
• Build Scalable Pipelines: maintain large-scale batch data pipelines with Dataflow, BigQuery, and Airflow/Composer.
• Develop Core Capabilities: enhance platform with experiment targeting and scalable localized assignments.
• Optimize Data Infrastructure: drive efficiency across pipelines, frameworks, and queries.
• Stay Current with Industry Trends: integrate latest analytics and cloud tech for the platform.
• Mentor and Guide: provide leadership to junior engineers.
• Collaborate on Experiment Analysis: work with marketers, analysts, and data scientists on metrics and methods.

Skills

Scala

Python

SQL

Data pipelines

BigQuery

Education

Bachelor's or Master's in CS/Statistics

Tools

Spark

Flink

Google Dataflow

BigQuery

Airflow/Composer

Job description

Overview

We are seeking a highly motivated and experienced Staff Software Engineer to lead the data architecture and engineering strategy for our experimentation platform. In this role, you will build and scale the data systems that enable reliable, consistent, and timely experimentation insights across the company. You will partner with data scientists, analysts, product managers, and other engineering teams to design robust data models, pipelines, and governance practices that support thousands of metrics and diverse statistical methodologies.

Overview

We are seeking a highly motivated and experienced Staff Software Engineer to lead the data architecture and engineering strategy for our experimentation platform. In this role, you will build and scale the data systems that enable reliable, consistent, and timely experimentation insights across the company. You will partner with data scientists, analysts, product managers, and other engineering teams to design robust data models, pipelines, and governance practices that support thousands of metrics and diverse statistical methodologies.

This is a high-impact, high-visibility position that demands strong software engineering and data engineering expertise, architectural leadership, and a passion for building highly available and performant distributed systems. You will define the technical roadmap and strategy for the experimentation platform’s analytics infrastructure, ensuring alignment with business objectives while pushing the boundaries of what’s possible in experimentation and data analysis.

Our experimentation platform is a distributed, highly available, low latency Finagle service. The platform leverages Google Cloud technologies, including Cloud Composer for workflow orchestration, Dataflow for data processing, and BigQuery for data warehousing. Our statistical engine applies a combination of Python libraries (e.g., Statsmodels, SciPy) and custom algorithms to analyze experiment results.

Responsibilities

• Define Technical Strategy Provide the roadmap and architecture for the experimentation platform’s infrastructure, ensuring alignment with business objectives and adherence to industry best practices.
• Develop Near Real-Time Systems Lead critical initiatives to build our next-generation near real-time ecosystem, to enhance near real-time observability and alerting, leveraging Scala, Pub/Sub, Akka, and Dataflow on Google Cloud.
• Build Scalable Pipelines Architect and maintain large-scale batch data pipelines using Google Dataflow, BigQuery, and Airflow/Cloud Composer to handle high-volume, batch data processing.
• Develop Core Capabilities Enhance the experimentation platform with new capabilities such as experiment targeting and localized assignments at scale to reduce latency and improve developer experience.
• Optimize Data Infrastructure Drive efficiency and performance improvements across experimentation pipelines, frameworks, and query layers. Evaluate trade-offs in system design, balancing speed, scalability, cost, and accuracy.
• Stay Current with Industry Trends Research, evaluate, and integrate the latest advancements in experimentation methods, data analysis techniques, and cloud-based technologies to continually improve the platform.
• Mentor and Guide Provide technical leadership and support to junior engineers, fostering a culture of continuous learning and professional growth.
• Collaborate on Experiment Analysis Partner with marketers, analysts, and data scientists to build infrastructure that supports thousands of metrics and various statistical methods (e.g., t-tests, sequential testing, Bayesian analysis).

Qualifications

• Bachelor's or Master's degree in Computer Science, Statistics, or a related field.
• 10+ years in software engineering, with a focus on data engineering and data architecture.
• Proficiency in Scala, Python, and SQL.
• Demonstrated success building and maintaining large-scale data pipelines using technologies such as Spark, Flink, Google Dataflow, BigQuery, or Airflow/Composer.
• Familiarity with Python libraries for statistical analysis (e.g., Statsmodels, SciPy).
• Deep understanding of software development lifecycle best practices, including agile methodologies.
• Excellent communication, collaboration, and stakeholder management skills.
• Proven ability to lead complex projects and mentor engineering teams.
• Proven expertise in A/B testing methodologies and statistical concepts.

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.

The Expected Base Pay Range For This Position Is

Oakland $202,500 - $274,000