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.

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

Robust 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

📌 Data Engineer (Sydney)
🏢 Uniquehire
📍 Sydney