Data Engineer Salary Guide

  • Entry Level $100,000 AUD
  • Mid Level $135,000 AUD
  • Senior Level $170,000 AUD


  • Design, develop, and maintain optimal data pipeline architectures.
  • Collaborate with data scientists and analysts to transform data into formats suitable for analytics.
  • Ensure high availability and performance of data infrastructure, including databases, data lakes, and big data platforms.
  • Stay updated with the latest data storage and retrieval methodologies.
  • Implement automation and data validation processes to increase efficiency and data integrity.

Key Skills

  • Proficiency in big data tools like Hadoop, Spark, and Kafka.
  • Experience with relational SQL and NoSQL databases, including Postgres, Cassandra, and MongoDB.
  • Ability to design and implement ETL (Extract, Transform, Load) processes.
  • Knowledge of data warehousing solutions such as Amazon Redshift or Google BigQuery.
  • Familiarity with data pipeline and workflow management tools like Apache NiFi and Apache Airflow.

Standard Industry Training for Data Engineers

  • Google Cloud Professional Data Engineer Certification
  • Azure Data Engineer Associate Certification
  • AWS Certified Big Data – Specialty

Interview Questions:

  1. How do you ensure that the data in a pipeline is accurate and reliable?
  2. Describe a situation where you had to handle a large influx of data in real-time. How did you manage it?
  3. What are the key considerations when migrating data from a traditional relational database to a big data platform?
  4. How do you handle schema changes or evolving data structures in your pipelines?
  5. Discuss a challenging problem you faced in data integration and how you solved it.
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