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

Join Intuit’s Business Intelligence (BI) Platform team as we reimagine the next generation of scalable, intelligent data infrastructure. We serve over 240TB of data, 2 billion records daily, and deliver 200+ million report requests through 20+ complex pipelines—supporting enterprise and mid-market customers on their most critical decisions.

Responsibilities

Design and implement robust, scalable data pipelines optimized for low-latency and high-throughput use cases.
Drive architecture decisions and evolve our platform toward polyglot persistence patterns, including RDBMS, NoSQL, key-value, time-series, and OLAP systems (e.g., Druid).
Lead real-time data processing initiatives, including stream-based ingestion and transformation (e.g., with Kafka/Flink/Spark Streaming).
Own data modeling strategies—normalized vs. denormalized schema design, schema evolution, and storage optimization.
Scale our data layer to support large-scale multi-entity reporting and cloud-native architectures on AWS (e.g., Redshift, S3, Glue).
Collaborate with engineering and product stakeholders to align platform capabilities with business needs and SLAs.
Plan and execute migration strategies for legacy systems, ensuring a smooth path toward modernization and system deprecation.

Enforce best practices across data governance, testing, CI/CD, and observability to maintain operational excellence.
Contribute to internal tooling that boosts engineering productivity, visibility, and reliability of the data platform.

Qualifications

12–15 years of experience in data engineering with deep expertise in distributed, cloud-native systems.
Proven experience designing and operating systems leveraging polyglot storage models (OLAP, NoSQL, key-value, etc.).
Strong knowledge of OLTP and OLAP workloads, including best practices in bridging real-time and batch processing paradigms.
Demonstrated success scaling platforms for high concurrency, large data volumes, and tight latency requirements.
Expert in data modeling, schema evolution, and designing for resilience and extensibility.
Strong working knowledge of AWS data services (Redshift, S3, Glue, Athena, EMR) and performance optimization.
Experienced in planning and executing migration strategies and system deprecation in enterprise environments.
Effective collaborator and communicator with a track record of influencing across product, data science, and engineering teams.
Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related technical field.Why

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