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Software Engineer 2, Data Engineering

Intuit

$124,500 - $186,500
Aug 15, 2025
Mountain View, CA, US
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Intuit is looking to leverage big data technologies to gain insights into customer experiences by building data frameworks, ingestion pipelines, and tools using platforms such as Google Cloud, Hive, and AWS Big Data Systems.

Requirements

  • Proficiency in developing Software for Java (Spring & Springboot), Scala for spark streaming & spark applications, or other JVM based languages.
  • Working Knowledge of SQL, XML, JSON, YML, very strong Python and Linux.
  • Knowledgeable with tools and frameworks Docker, Spark, Scala, Jupiter Notebook, Databricks Notebook, Kubernetes, Feature Management Platforms, SageMaker.
  • Advanced experience with scripting language – Python or Shell is a must have.
  • Experience with cloud platforms such as AWS, Azure or GCP - Amazon web services: EC2, S3, and EMR (Elastic Map Reduce) or equivalent cloud computing approaches.
  • Strong expertise in Data Warehousing and analytic architecture.
  • Experience working with large data volumes, data visualization.

Responsibilities

  • 70-85% hands-on development in all phases of the software life cycle.
  • Rapidly fix bugs and solve problems.
  • Code reviews and Defect remediation.
  • Clean, transform and validate data for use in analytics and reporting.
  • Monitor data quality and pipeline performance, troubleshoot and resolve data issues.
  • Collaborates effectively with senior engineers and architects to solve problems spanning their respective areas to deliver end-to-end quality in our technology and customer experience.
  • Designing/developing ETL jobs across multiple big data platforms and tools including S3, EMR, Hive, Spark SQL, PYSpark.

Other

  • BS or MS in Computer Science, Data Engineering or related field.
  • 2+ years of core development experience.
  • Experience with Agile Development, SRUM, and/or Extreme Programming methodologies.
  • Influences and communicates effectively.
  • Actively stay abreast of industry best practices, share learnings, and experiment and apply cutting edge technologies while proactively identifying opportunities to enhance software applications with AI technology.