Intuit is the global financial technology platform that powers prosperity for the people and communities we serve. With approximately 100 million customers worldwide using products such as TurboTax, Credit Karma, QuickBooks, and Mailchimp, we believe that everyone should have the opportunity to prosper. We never stop working to find new, innovative ways to make that possible.

Job Overview

Intuit is revolutionizing the mid-market with its most pivotal offering, the Intuit Enterprise Suite (IES)—a groundbreaking, all-in-one solution engineered to empower businesses with complex needs to achieve unparalleled growth, streamline operations, and scale with confidence. This integrated platform eradicates the inefficiencies of managing multiple applications by delivering AI-powered insights, boosting productivity through smart automation, and ensuring compliant, error-free operations. IES provides comprehensive capabilities including advanced financial management, integrated payroll and payments, automated marketing, third-party app access, and expert support, with industry-specific customizations for construction, non-profit, and project-based businesses in the U.S., marking a monumental stride in Intuit's strategy to capture the global mid-market opportunity.

Come join the Mid-Market Data team as a Staff Software Engineer. As a Staff Software Engineer, you will play a pivotal role in driving the data strategy and delivery for the Mid-Market Data team. Your expertise in cloud platforms and Big Data technologies plus AI will be crucial in providing technical leadership, solving complex data challenges, and aligning our data infrastructure with strategic business objectives.You will play a key role in every stage of the product lifecycle, working across a diverse portfolio of data, software products, and services. Leveraging your deep expertise in software engineering methodologies and industry best practices for data products, you will contribute to the design, development, and optimization of Intuit’s data solutions. Your specialized skills will be instrumental in enhancing onboarding experiences, eliminating barriers to entry, and ensuring seamless maintenance and scalability of data-driven solutions.

Responsibilities

Drives velocity in the organization by accelerating customer, business, and technology outcomes by identifying and driving key opportunities across the company.
Understands customer behaviors and partners with cross-functional partners to influence and drive end-to-end solutions for customer problems.
Executes with a boundaryless mindset and contributes to solutions outside of their primary area of ownership.
Develop, design, and implement robust, high-performance data pipelines—both batch and real-time—along with scalable data platforms on AWS Cloud. Ensure reliability and scalability while maintaining comprehensive test coverage to deliver seamless and efficient data processing solutions.
Knowledge of building AI native applications
- Demonstrated ability to embrace the rapid evolution of the AI space as part of the development process.
- High-level understanding of how traditional AI + genAI models work, the different types of AI models that exist, and their pros and cons.
- Understanding of the latest tools and technologies that apply AI to real-world applications.
Research and maintain a deep knowledge of the industry, including trends and technologies, so that you can identify strategy opportunities and contribute to thought leadership best practices.
Stay up to date with emerging data technologies and best practices, ensuring continuous improvement of Enterprise Data architectures.
Team/ Collaboration:
- Collaborate with cross-functional teams, including product teams, AI and Data Science teams, data architects, and DevOps teams, to align data engineering strategies with enterprise goals.
- Be seen as a key innovation and thought leader in the AI and Data space, able to recommend and promote both platform and process improvements within Intuit’s AI and Data infrastructure, in order to lift the entire organization.
- Works with cross-functional team members from Architecture, Product Management, and Operations to design, develop, test, and release features
- Optimization: Look for opportunities to drive step-changes in performance and cost reduction, through new platforms or tuning of existing platforms.

Qualifications

BS or MS in Computer Science, Data Engineering, or a related field.
8+ years of experience in software engineering, Data Engineering, or AI development, with significant time spent in Staff engineering roles
Expertise in developing Data Modelling, Data warehousing, Data pipelines development using frameworks like Apache Spark, Hadoop, and streaming technologies such as Kafka.
Familiarity with data models, data structures, and schema management.
Understanding batch data processing constructs.
Strong background using cloud platforms such as AWS, Azure, or GCP, including Amazon Web Services: EC2, S3, EMR (Elastic Map Reduce), RedShift, DynamoDB, and Athena, or equivalent cloud computing approaches. Expertise with AWS is specifically a must-have
Proven ability in designing, building, and shipping complex, data-intensive, and scalable systems in a real-world setting.
Experience with Big-Data Technologies and low-latency NoSQL datastores (Hive, HBase, Spark, Kafka, Storm, MapReduce, HDFS, Splunk, Zookeeper, MemSQL, Cassandra, Redshift, GraphDB), understands the concepts and technology ecosystem around both real-time and batch processing in Hadoop
Hands-on experience with Generative AI models (LLMs, diffusion models), frameworks (LangChain, Hugging Face, OpenAI/Anthropic APIs), and RAG implementations.
Ability to work effectively in a fast-paced, collaborative, and innovative environment, understanding organizational dynamics and decision pathways.
Strong understanding of Agentic AI systems .
Build solid & scalable architecture to address normalization, lineage, data governance, ontology and data discoverability use cases
Drive alignment between enterprise architecture and business needs.
Conduct quick proof-of-concepts (POCs) for feasibility studies and take them to the production
Lead by example, demonstrating best practices for unit testing, test automation, CI/CD performance testing, capacity planning, documentation, monitoring, alerting, and incident response
Effective listening skills and strong collaboration to lead change by example and through influence