AWS Data Engineer
Posted 3
views
Summary
Designs, builds, and maintains end-to-end batch and near real-time data pipelines on AWS (S3, Glue, Redshift, Athena, EMR) using SQL, Python/PySpark, dbt, and Airflow orchestration. Also handles data quality checks, monitoring/alerting, DataOps/CI-CD practices, technical documentation, and production support.
Role : AWS Data Engineer
Type of role - Permanent Position
Location : Sydney, Australia
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
- No of years’ 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