A Data Engineer is responsible for designing, building, and maintaining scalable data architectures that support analytics, reporting, and machine learning initiatives. They develop and optimise data pipelines, ensure data quality and reliability, and collaborate with data scientists, analysts, and IT teams to enable data-driven decision-making across the organisation.
Data Engineer duties and responsibilities of the job
Data Engineers play a crucial role in transforming raw data into actionable insights. A Data Engineer job description typically includes:Designing, developing, and maintaining scalable ETL/ELT pipelines to process data from various sourcesBuilding and managing data warehouses, data lakes, or other storage solutions (on-premises or cloud)Ensuring data quality, integrity, and consistency through automated validation, cleansing, and testingCollaborating with data scientists and analysts to understand data requirements and deliver reliable datasetsOptimising database performance (e.g., partitioning, indexing, query tuning) for large-scale datasetsImplementing and enforcing data governance, security, and compliance standardsMonitoring data workflows and troubleshooting pipeline failures or performance bottlenecksDocumenting data models, architecture diagrams, and pipeline designsEvaluating and integrating new data technologies, tools, and frameworks to improve efficiencySupporting production deployments and providing on-call support for critical data systems
Typical Data Engineer duties
Strong candidates for Data Engineer roles typically hold a degree in one of the following fields:Computer Science or Software EngineeringInformation TechnologyData Science or Applied MathematicsStatistics or PhysicsEngineering (electrical, electronic, or related disciplines)Employers often look for the following technical skills and experiences:Proficiency in programming languages such as Python, Java, or ScalaHands-on experience with SQL and NoSQL databases (e.g., PostgreSQL, MySQL, MongoDB, Cassandra)Expertise in ETL/ELT tools and frameworks (e.g., Apache Airflow, AWS Glue, Talend, Informatica)Familiarity with big data technologies (e.g., Hadoop, Spark, Kafka)Experience with cloud platforms and services (e.g., AWS, Azure, GCP) for data storage and processingKnowledge of data modeling concepts and dimensional design for analyticsStrong understanding of data governance, privacy, and security best practicesExcellent problem-solving skills and attention to detailEffective communication skills to work with cross-functional teamsCertifications such as AWS Certified Data Analytics, Google Cloud Professional Data Engineer, or Microsoft Azure Data Engineer Associate are advantageous.
Looking for a data engineer or a data engineer job?
The candidate has little or no prior experience in the position and is still developing relevant skills.
50th Percentile
142500
The candidate has an average level of experience and has most of the necessary skills.
75th Percentile
161500
The candidate has above-average experience, has most or all the necessary skills and may have specialised qualifications.
Salary range based on three percentiles
The starting salaries represent gross yearly salaries. They do not include bonuses, benefits or superannuation.
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