Principal Software Engineer, Data Engineering

At Intuit, we are the global financial technology platform dedicated to powering prosperity for millions of people and the communities we serve. Our Consumer Group, home to iconic products like Turbo Tax and Credit Karma, is at the heart of this mission.

We are seeking a Principal Software Engineer, Data Engineering to act as the primary technical leader and domain expert across multiple Data Engineering teams. This role is crucial for driving strategic transformation and defining the foundational technology for our high-scale data products. This is a unique opportunity to shape the future of data engineering architecture for the Consumer Group.

The successful candidate must operate with a 'Builder Profile,' possessing the mastery to identify and execute key opportunities that significantly boost engineering velocity across the company. This is a senior Individual Contributor (IC) role—no direct reports—where you will apply master-level software engineering and industry best practices to solve complex, end-to-end technical challenges, leading decisions that have a significant long-term impact.

Strategy & Vision:
Lead the implementation of the Consumer Platform data strategy, driving significant, end-to-end initiatives that align with the target state architecture.

Design Authority:
Provide a 'bird's-eye architecture view,' clarifying rationale for all major Technical Decisions. Drive the design and implementation of durable, scalable, and secure technical designs for high availability.

Hands-On Execution:
Act as a role model in full-cycle engineering, engaging in approximately 70% hands-on development. Lead critical projects (PoCs, pilot implementations) and mentor through live coding and collaborative problem-solving.

Platform Standards:
Define the technical strategy for data modeling, ingestion, curation, and data quality across the consumer platform. Establish the target state for real-time analytics pipelines.

Efficiency:
Define and enforce technical standards and clear 'paved paths' to accelerate velocity. Provide direction on smart execution to reduce rework and infrastructure overhead (e.g., optimizing EMR management).

Reliability:
Define approaches for incident avoidance and 'game day' preparedness, including establishing Data Quality (DQ) monitoring and comprehensive E2E testing. Ensure designs scale for future data volumes while optimizing infrastructure cost.

AI Leadership:
Possess the deep knowledge required for building and scaling AI native applications. Guide the applicability of AI/GenAI to customer problems, understanding both its value and limitations.

Champion Innovation:
Champion and integrate emerging technologies, leveraging AI and Agentic experiences to create new opportunities and improve customer experiences.

Tooling Strategy:
Provide direction to consolidate and manage the multiplicity of AI tooling silos to reduce organizational inefficiency.

Influence:
Influence technology and product direction, typically driving work across multiple scrum teams. Act as the essential bridge between Product and Data Engineering, ensuring clear business context (including data consumption patterns) is provided.

Mentorship:
Provide comprehensive technical mentorship through personalized skill enhancement. Conduct detailed architecture and code reviews (including paired programming) to enforce standards and share advanced design principles.

Community:
Contribute to standards, patterns, and best practices that elevate the entire engineering community.

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

Influence & Communication:
Strong ability to influence and communicate clearly, succinctly, and persuasively with technical, product, and non-technical partners at all levels.

Strategic Mindset:
Ability to synthesize achievable solutions from diverse inputs and complex data. Demonstrated experience driving technical strategy in high-scale environments.

Abstraction:
Abilit…