Senior Software Engineer

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.

We are seeking a Senior Data Engineer to join our Data Platform team, with a focus on designing robust data models, building scalable ETL/ELT pipelines, and enabling trustworthy, high-quality data for analytics, reporting, and intelligent systems.In this role, you will play a critical part in evolving our data architecture, ensuring data quality, and building integrations that power analytics and decision-making across the business.

What you'll bring

• 6+ years of hands-on experience in data engineering or data platform development.
• Strong experience in building and optimizing data pipelines using Spark and Flink.
• Proficiency with DBT for transformation workflows and Kafka for event-driven ingestion.
• Solid understanding of data modeling principles and best practices in relational and analytical systems.
• Proven track record in creating and maintaining historical, delta, and snapshot data structures.
• Familiarity with data quality frameworks and tools for validation and anomaly detection.
• Experience working with columnar file formats and scalable data storage systems.
• Strong coding skills in Python or Scala, and familiarity with SQL at scale.
• Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field.
  
  

How you will lead

• Design and implement scalable ETL and ELT pipelines using tools like Apache Spark, DBT, and Kafka.
• Own the development of data models that support reporting, analytics, and machine learning use cases.
• Build and maintain historical, delta, and snapshot tables optimized for large-scale data processing and access patterns.
• Work with columnar storage formats (e.g., Parquet, ORC) to optimize performance and storage efficiency.
• Integrate and automate data validation and quality checks, ensuring trust and accuracy across pipelines.
• Partner with data platform and product teams to design and deliver seamless data integrations across systems and domains.
• Contribute to data governance practices, schema evolution, and performance tuning.