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.