Principal Software Engineer, Data Engineering
Intuit
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Benefits offered by this job
Job summary
Qualifications
- 15+ years of software engineering experience, strong background in data engineering.
- 9+ years professional working experience with core big data technologies.
- Expert proficiency with SQL, Python, and Linux.
Responsibilities
- Lead implementation of the Consumer Platform data strategy.
- Provide 'bird's-eye architecture view' for major technical decisions.
- Act as a role model in full-cycle engineering, engaging in 70% hands-on development.
Skills
Education
Tools
Job description
Join to apply for the Principal Software Engineer, Data Engineering role at Intuit
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 TurboTax 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.
- Technical Craft and Architectural Leadership
- 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.
- Data Strategy, Quality, and Velocity
- 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.
- Reliability: Define approaches for incident avoidance and "game day" preparedness, including establishing Data Quality (DQ) monitoring and comprehensive E2E testing.
- AI Native Applications and Innovation
- AI Leadership: Possess the deep knowledge required for building and scaling AI native applications.
- 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.
- Leadership, Mentorship, and Collaboration
- Influence: Influence technology and product direction, typically driving work across multiple scrum teams.
- Mentorship: Provide comprehensive technical mentorship through personalized skill enhancement and detailed architecture and code reviews.
- 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.
- Strategic And Leadership Skills
- 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: Operate at varying levels of abstraction from business strategy down to detailed technical design.
- Technical Expertise And Experience
- 15+ years of software engineering experience, strong background in data engineering.
- 9+ years professional working experience with core big data technologies:, Hadoop, Kafka, Columnar Databases, and Graph Databases. Strong experience with stream processing/near real-time data ingestion is essential.
- Expert proficiency with SQL, Python, and Linux. Expertise in JVM-based languages such as Java (Spring & Springboot) and Scala for Spark streaming/applications.
- Strong understanding of extensible data modeling for large-scale data and background in AWS. Expertise noted.
- Tools: Docker, Spark, Scala, Kubernetes, Databricks Notebooks, SageMaker.
- AI/ML: Background in AI skills in both traditional ML and Generative-AI applications.
- Methodology: Agile Development, SCRUM, and/or Extreme Programming.
- Compensation and Benefits
Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs. 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:
- Bay Area California $254,500 – 344,000
- Southern California $242,000 – 327,500
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