Job Description
My client is seeking a Senior Data Engineer with strong 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 modern cloud data platforms and high-volume data processing.
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Key Responsibilities
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Design, develop and maintain scalable batch and real-time data ingestion frameworks.
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Build and optimise data pipelines using Python, PySpark, SQL and AWS services.
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Develop streaming solutions using AWS Kinesis, Kafka or similar technologies.
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Build and manage workflows using Apache Airflow (MWAA).
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Work with Amazon Redshift, RDS, S3 and NoSQL databases to support data warehousing and analytics.
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Implement data transformation, monitoring and observability solutions.
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Collaborate with cross-functional teams to deliver secure, high-performing data solutions.
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Must Have Skills
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Strong commercial experience with AWS data services.
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Advanced Python, PySpark and SQL skills.
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Experience with Apache Airflow (MWAA).
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Hands‐on experience with Amazon Redshift.
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Experience building batch and real-time data pipelines.
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Solid knowledge of AWS Kinesis, Kafka or other streaming technologies.
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Experience working with Data Warehouses and Data Lake/Lakehouse architectures.
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Experience with relational and NoSQL databases such as Oracle, Snowflake, MongoDB, DynamoDB or Teradata.
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Nice to Have
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Experience with AWS SageMaker Unified Studio.
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Exposure to ML or Generative AI pipelines.
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Data modelling experience.
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Infrastructure as Code using Terraform or CloudFormation.
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CI/CD and Git experience.
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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 (New South Wales)
🏢 Paxus
📍 New South Wales