My client is seeking a Senior Data Engineer with solid AWS data engineering experience to design, build and optimise scalable data pipelines across both batch and real-time environments. This role is ideal for someone with expertise in Python, PySpark, Airflow and AWS services, who enjoys working on up-to-date cloud data platforms and high-volume data processing.

Key Responsibilities
Design, develop and maintain scalable batch and real-time data ingestion frameworks.
Build and optimise data pipelines using Python, PySpark, SQL and AWS services.
Develop streaming solutions using AWS Kinesis, Kafka or similar technologies.
Build and manage workflows using Apache Airflow (MWAA).
Work with Amazon Redshift, RDS, S3 and NoSQL databases to support data warehousing and analytics.
Implement data transformation, monitoring and observability solutions.
Collaborate with cross-functional teams to deliver secure, high-performing data solutions.

Must Have Skills
Strong commercial experience with AWS data services.
Advanced Python,



PySpark and SQL skills.
Experience with Apache Airflow (MWAA).
Hands‑on experience with Amazon Redshift.
Experience building batch and real-time data pipelines.
Solid knowledge of AWS Kinesis, Kafka or other streaming technologies.
Experience working with Data Warehouses and Data Lake/Lakehouse architectures.
Experience with relational and NoSQL databases such as Oracle, Snowflake, MongoDB, DynamoDB or Teradata.

Nice to Have
Experience with AWS SageMaker Unified Studio.
Exposure to ML or Generative AI pipelines.
Data modelling experience.
Infrastructure as Code using Terraform or CloudFormation.
CI/CD and Git experience.

Paxus values diversity and welcomes applications from Indigenous Australians, people from diverse cultural and linguistic backgrounds and people living with a disability.
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📌 Data Engineer City Of Sydney
🏢 Paxus
📍 City of Sydney