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 up-to-date data engineering practices.

Key Responsibilities

- Design, develop, and maintain end to end data pipelines (batch and near real time) on AWS Data Platform
- Build and manage ETL/ELT workflows using AWS services (e.g., AWS Glue, S3, Redshift, Athena, EMR), dbt and orchestration tools such as Airflow
- Implement data ingestion patterns from diverse sources (databases, APIs, files, event streams) into lake/warehouse layers such as raw, cleansed, and curated data layers
- Develop transformation logic using SQL and Python/PySpark for cleansing, enrichment, and standardisation
- Implement robust data quality checks, reconciliation controls, and monitoring/alerting for failures and anomalies
- Collaborate with data analysts/data scientists to model datasets for analytics and machine learning consumption.
- Contribute to DataOps/DevOps practices: version control, CI/CD, automated testing, release management, and operational support.
- Produce and maintain technical documentation (data flows, mappings,



job schedules, runbooks, and operational procedures)
- Optimise Data Pipeline performance and Support workflow orchestration and scheduling
- Support production deployments and operations

Required Skills & Experience

- 7 experience as a Data Engineer
- Advanced SQL skills
- Hands on experience working with Teradata and Siebel CRM data sets
- Experience delivering data pipelines in a large scale enterprise data platform environment
- Strong hands on AWS experience with common data services such as : Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR and dbt
- Strong programming capability in Python and strong data transformation experience using PySpark (preferred) and/or Spark.
- Advanced SQL skills (query optimisation, complex joins, window functions, performance tuning)
- Experience with workflow orchestration tools such as Airflow
- Solid understanding of data warehousing concepts (dimensional modelling, partitioning, incremental loads, CDC concepts).
- Experience implementing monitoring, logging, alerting, and operational support processes.
- Strong communication skills and ability to work with stakeholders to translate requirements into data deliverables

Telco Industry Experience is highly desirable

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📌 Data Engineer - AWS (City of Sydney)
🏢 CareCone Group
📍 City of Sydney