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

Come join the BI team at Intuit India as we build the next generation of data infrastructure powering mission-critical analytics and insights for our enterprise and mid-market customers. Our BI platform processes 2 billion records daily, supports over 240TB of data, and serves 200+ million reporting requests through a complex landscape of 20+ data pipelines.We are looking for a Principal Data Engineer who thrives on solving low latency, high-throughput data processing challenges at scale and can lead architectural evolution across a high-impact platform.

Responsibilities

Design and implement high-performance, low-latency data processing pipelines to power analytics and reporting at petabyte scale.
Lead platform-wide technical initiatives to improve latency, scalability, reliability, and throughput of our BI infrastructure.
Provide technical leadership across multiple interdependent teams and influence architectural decisions for both batch and stream processing workloads.
Partner closely with PMs, Analysts, and Data Scientists to ensure platform SLAs align with evolving business goals.
Identify and drive system-wide optimizations—introducing durable, observable, and cost-effective patterns across ingestion, transformation, and consumption layers.
Develop internal tools to automate operations, enhance observability, and maintain system health at scale.
Champion engineering and operational excellence by enforcing best practices in data modeling, testing, and CI/CD.
Shape future data product strategy by contributing to the evolution of Intuit's enterprise data architecture.

Qualifications

15+ years of experience in data engineering, with deep expertise in building and scaling distributed data systems.
Strong hands-on experience in stream processing technologies (e.g., Apache Flink, Spark Streaming, Kafka Streams).
Deep understanding of data formats such as Parquet, Delta Lake, Iceberg, and performance tuning across large datasets.
Demonstrated experience solving challenges in throughput, query latency, and SLA adherence across large-scale BI platforms.
Proven ability to design for observability: metrics, alerting, and root cause analysis.
Sound architectural instincts—able to assess tradeoffs and design for long-term maintainability.
Strong mentoring and technical leadership skills with the ability to influence across org boundaries.
Exceptional collaboration and communication skills across time zones and technical functions.
Comfortable working in high-ownership, high-autonomy environments with evolving priorities.
Bachelor’s or Master’s degree in Computer Science, Engineering, or equivalent field.