Data Engineer

Who you are

• We are seeking engineers who bring deep analytical rigor to data modeling and relentless dedication to data quality
• Solid Academic & Technical Foundation: Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical field (or equivalent practical experience)
• Early Career Momentum: 2-3 years of hands-on experience in data engineering, BI engineering, or equivalent project-driven environments
• SQL Mastery: Exceptional ability to write clean SQL (joins, filters, aggregations) and query across cloud data platforms like Snowflake, BigQuery, or Databricks
• ETL/ELT Aptitude: Strong foundational understanding of data transformation concepts and comfort navigating tools like dbt and Airflow under guided supervision
• Code & Version Control Literacy: Familiarity with Python (or similar languages) and Git-based collaborative workflows to ship clean code efficiently

What the job involves

• As our Data Engineer I, you will build and fine-tune the robust cloud data pipelines that power our analytics, data science, and product teams
• You’ll tackle real-world architecture challenges, collaborate with world-class engineers, and directly shape how intelligent learning experiences reach millions of users worldwide
• Engineer the Flow: Implement high-performance ELT/ETL transformations using tools like dbt based on innovative specs from senior engineers and architects
• Orchestrate Data Jobs: Build, deploy, and maintain seamless data ingestion and transformation jobs powered by Airflow and Cloud Composer
• Guard Data Quality: Write and execute vital data tests—including row counts, null checks, and reference validations—to keep our datasets clean and trusted
• Monitor & Resolve: Proactively investigate automated alerts, ensuring peak system performance and escalating deeper architectural changes when needed
• Shape Lakehouse Architecture: Maintain pristine schemas and naming conventions across Snowflake, BigQuery, or Databricks while implementing table and view enhancements
• Document & Empower: Create clear documentation for pipelines, schemas, and caveats so the entire engineering team can safely build on your foundation
• Partner & Collaborate: Work closely alongside Analytics and Data Science teams to deeply understand their query needs and deliver optimized data structures