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Common mid-level data-engineer title with 3–5 years increases applicant density moderately.
Core data engineering skills are transferable across industries, though public-education domain experience is advantageous.
Explicit 3–5 years plus mandatory AWS, Spark, SQL and data-pipeline skills create strict filters.
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Design and maintain scalable ETL/ELT data pipelines and architectures using AWS to support large-scale data ingestion and analytics.
Develop and optimize Spark/PySpark jobs for batch and real-time data processing.
Collaborate with analytics, product, and engineering teams to ensure data availability and quality for reporting and insights impacting millions of students.
Bachelor's degree in Computer Science, IT, Data Engineering, or related field.
3-5 years of hands-on experience in data engineering, data pipeline development, or cloud-based data systems.
Strong skills in SQL, Python or PySpark, and practical experience with AWS data stack including S3, Glue, Lambda, Redshift, Athena, EMR, and Step Functions.
Understanding of data lake architecture, ETL/ELT frameworks, data warehousing concepts, and big data tools like Spark SQL and Delta Lake.
Experienced data engineer with practical knowledge of AWS ecosystem and big data frameworks, capable of handling end-to-end data pipeline development and optimization.
Able to work collaboratively with multi-disciplinary teams to translate business needs into reliable data assets impacting large scale educational data.
Has exposure to data governance, monitoring, CI/CD automation, and performance tuning in data systems to ensure high-quality, production-grade data delivery.