Senior Azure Data Platform Engineer

Showtime Consulting; Sydney; On-site; AUD 120,000 - 180,000; Full time

Job summary

Showtime Consulting is seeking an experienced Data Engineer to design, build, and support enterprise-scale data platforms in Microsoft Azure. You will work across data engineering, data warehousing, cloud analytics, and modern data architectures to deliver trusted, scalable data solutions for reporting and decision-making.

The role emphasises designing efficient pipelines, improving data quality, implementing governance controls, and driving best practices across the data ecosystem within an

Qualifications

• Minimum 5 years' experience in Data Engineering, Data Warehousing, or a related data-focused role.
• Demonstrated experience designing, developing, and maintaining scalable data pipelines on Azure.
• Strong SQL, data modelling, ETL/ELT, governance, and collaboration with stakeholders.

Responsibilities

• Design, build, and support enterprise-scale data platforms and data pipelines.
• Implement and optimize Azure-based data solutions using ADF, Databricks, ADLS, Synapse, and SQL.
• Develop data integration frameworks to ingest, transform, and deliver data from multiple sources.
• Create reusable data engineering frameworks and automation processes.
• Monitor, troubleshoot, and optimise data pipelines for performance and reliability.
• Collaborate with business and technical stakeholders to translate requirements into data models.

Skills

Data engineering

Azure

Python

PySpark

SQL

Data warehousing

Tools

Azure Data Factory

Azure Databricks

Azure Data Lake Storage

Azure Synapse Analytics

Azure SQL

Python

SQL

Job description

Showtime Consulting is seeking an experienced Data Engineer to design, build, and support enterprise-scale data platforms in Microsoft Azure. You will work across data engineering, data warehousing, cloud analytics, and modern data architectures to deliver trusted, scalable data solutions for reporting and decision-making.

The role emphasises designing efficient pipelines, improving data quality, implementing governance controls, and driving best practices across the data ecosystem within an