Job Description

Role : Data Engineer - AWS
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Location : Sydney, Australia

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Role Overview

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The Senior AWS Data Engineer is responsible to design, build, and support scalable data pipelines and curated datasets on AWS. He/She will work with cross functional teams to ingest, transform, and serve data for reporting, analytics, and downstream applications. The ideal candidate is hands on, strong in SQL/Python, and experienced with AWS native data services and modern data engineering practices.

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Key Responsibilities

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Design, develop, and maintain end to end data pipelines (batch and near real time) on AWS Data Platform

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Build and manage ETL/ELT workflows using AWS services (e.g., AWS Glue, S3, Redshift, Athena, EMR), dbt and orchestration tools such as Airflow

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Implement data ingestion patterns from diverse sources (databases, APIs, files, event streams) into lake/warehouse layers such as raw, cleansed, and curated data layers

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Develop transformation logic using SQL and Python/PySpark for cleansing, enrichment, and standardisation

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Implement robust data quality checks, reconciliation controls, and monitoring/alerting for failures and anomalies

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Collaborate with data analysts/data scientists to model datasets for analytics and machine learning consumption.

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Contribute to DataOps/DevOps practices: version control, CI/CD, automated testing, release management, and operational support.





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Produce and maintain technical documentation (data flows, mappings, job schedules, runbooks, and operational procedures)

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Optimise Data Pipeline performance and Support workflow orchestration and scheduling

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Support production deployments and operations

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Required Skills & Experience 7 experience as a Data Engineer Advanced SQL skills

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Hands on experience working with Teradata and Siebel CRM data sets

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Experience delivering data pipelines in a large scale enterprise data platform environment

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Strong hands on AWS experience with common data services such as : Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR and dbt

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Strong programming capability in Python and solid data transformation experience using PySpark (preferred) and/or Spark.

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Advanced SQL skills (query optimisation, complex joins, window functions, performance tuning)

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Experience with workflow orchestration tools such as Airflow

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Solid understanding of data warehousing concepts (dimensional modelling, partitioning, incremental loads, CDC concepts).

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Experience implementing monitoring, logging, alerting, and operational support processes.

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Strong communication skills and ability to work with stakeholders to translate requirements into data deliverables

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Telco Industry Experience is highly desirable

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