Databricks Data Engineer (Hybrid) – Modern Lakehouse

TalentReady

Sydney

Hybrid

AUD 120,000 - 170,000

Full time

40 hours ago
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Job summary

TalentReady in Australia is seeking an experienced Data Engineer to join a major financial services organisation undergoing data modernisation. You will work within a modern Databricks environment, building scalable data pipelines and supporting the migration of legacy data platforms to a cloud-based lakehouse architecture.

You will collaborate with data engineers, analysts and business stakeholders to design, implement and optimise ETL/ELT processes, with a strong focus on quality and

Qualifications

  • Strong hands-on experience with Databricks, PySpark and SQL.
  • Experience with dbt, Delta Lake and lakehouse architecture.
  • Proven experience developing and maintaining ETL/ELT pipelines.
  • Cloud experience, ideally Azure or AWS.
  • Understanding of CI/CD, data testing and production support.
  • Experience in insurance or financial services highly regarded.

Responsibilities

  • Design, develop and optimise data pipelines using Databricks, Python/PySpark and SQL.
  • Build and maintain data transformations using dbt and Delta Lake.
  • Develop scalable data models across Bronze, Silver and Gold layers.
  • Support data migration from legacy platforms, including SAS.
  • Implement data quality checks, testing and performance optimisation.
  • Collaborate with data engineers, analysts and business stakeholders.

Skills

Databricks
PySpark
SQL
dbt
Delta Lake
Lakehouse
ETL/ELT
Azure
AWS
CI/CD

Tools

GitHub Copilot
Databricks Genie

Job description

TalentReady in Australia is seeking an experienced Data Engineer to join a major financial services organisation undergoing data modernisation. You will work within a modern Databricks environment, building scalable data pipelines and supporting the migration of legacy data platforms to a cloud-based lakehouse architecture.

You will collaborate with data engineers, analysts and business stakeholders to design, implement and optimise ETL/ELT processes, with a strong focus on quality and

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