Data Engineer

XPT Software; Sydney; Presencial; AUD 150.000 - 190.000; Jornada completa

Descripción de la vacante

XPT Software in Sydney is seeking a Senior Data Engineer to design, build, and optimise scalable data pipelines and lakehouse solutions using Azure Databricks and the wider Microsoft Azure data stack.

You will develop production-grade pipelines, implement lakehouse patterns, manage data quality, and collaborate with architects and stakeholders to deliver reliable data products at scale.

Formación

• 8+ years of experience in data engineering, data integration, ETL/ELT, or large-scale data platform delivery.

Responsabilidades

• Design, build, and optimise scalable batch and streaming data pipelines using Azure Databricks, PySpark, Python, SQL, and Delta Lake.
• Develop reusable ingestion, transformation, validation, and publishing frameworks for large-scale enterprise datasets.
• Implement medallion or layered lakehouse patterns across raw, curated, and consumption-ready data zones.
• Integrate Azure Databricks with Azure Data Lake Storage Gen2, Azure Data Factory, Azure Key Vault, Azure DevOps, and related Azure services.

Conocimientos

Azure Databricks

Python

PySpark

SQL

Delta Lake

Azure Data Lake Gen2

Azure Data Factory

Azure DevOps

Dimensional Modeling

Data Warehousing

Data Lake Design

Metadata & Lineage

Partitioning

Parquet

CI/CD

Herramientas

Terraform

Bicep

dbt

Airflow

Kafka

Power BI

Descripción del empleo

We are looking for a Senior Data Engineer with strong hands-on experience in Azure Databricks and the Microsoft Azure data ecosystem. The role will design, build, optimise, and support scalable data pipelines and lakehouse solutions that enable analytics, reporting, data science, and enterprise decision-making.

What You'll Work On

• Design, build, and optimise scalable batch and streaming data pipelines using Azure Databricks, PySpark, Python, SQL, and Delta Lake.
• Develop reusable ingestion, transformation, validation, and publishing frameworks for large-scale enterprise datasets.
• Implement medallion or layered lakehouse patterns across raw, curated, and consumption-ready data zones.
• Integrate Azure Databricks with Azure Data Lake Storage Gen2, Azure Data Factory, Azure Key Vault, Azure DevOps, and related Azure services.
• Develop production-grade Databricks notebooks, jobs, workflows, libraries, and cluster configurations.
• Create complex SQL and PySpark transformations, data-quality checks, reconciliation logic, and error-handling routines.
• Optimise Spark workloads, storage layouts, partitioning, file sizes, caching, and query performance while controlling platform cost.
• Implement Delta Lake capabilities such as ACID transactions, schema enforcement, schema evolution, time travel, and merge operations.
• Build automated CI/CD pipelines for code promotion, configuration management, testing, and deployment across environments.
• Apply data security and governance controls using role-based access, secrets management, data masking, auditability, and controlled access patterns.
• Monitor data pipelines, troubleshoot failures, perform root-cause analysis, and deliver sustainable production fixes.
• Partner with Architects, Business Analysts, Data Analysts, Product Owners, and business stakeholders to convert requirements into reliable data products.
• Provide technical leadership, code reviews, engineering standards, and mentoring to other data engineers.

Key Skills and Experience

• 8+ years of experience in data engineering, data integration, ETL/ELT, or large-scale data platform delivery.
• Strong hands-on experience with Azure Databricks, including notebooks, jobs or workflows, cluster configuration, debugging, and performance tuning.
• Advanced proficiency in Python, PySpark, and SQL for production-grade data engineering.
• Strong experience with Delta Lake and lakehouse architecture, including reliable incremental processing and data-quality controls.
• Practical experience with Azure Data Lake Storage Gen2 and Azure Data Factory.
• Experience implementing CI/CD using Azure DevOps, Git, branching strategies, automated testing, and environment-based deployment controls.
• Strong understanding of dimensional modelling, data warehousing, data lake design, metadata, lineage, partitioning, and file formats such as Parquet.
• Experience with orchestration, monitoring, alerting, operational support, incident analysis, and service-level expectations.
• Understanding of Azure identity, Key Vault, managed identities, networking, private endpoints, and secure data-platform patterns.
• Strong communication, documentation, problem-solving, code-review, and stakeholder collaboration skills.

Good to Have

• Experience with Microsoft Purview, Unity Catalog, Azure Synapse Analytics, Event Hubs, or Structured Streaming.
• Knowledge of infrastructure as code using Terraform or Bicep.
• Experience with dbt, Airflow, Kafka, Power BI, or machine-learning data pipelines.
• Experience in telecommunications or another high-volume enterprise environment.
• Relevant Microsoft Azure or Databricks certification

Job Details:

Role Overview

We are looking for a Senior Data Engineer with strong hands-on experience in Azure Databricks and the Microsoft Azure data ecosystem. The role will design, build, optimise, and support scalable data pipelines and lakehouse solutions that enable analytics, reporting, data science, and enterprise decision-making.

What You'll Work On

• Design, build, and optimise scalable batch and streaming data pipelines using Azure Databricks, PySpark, Python, SQL, and Delta Lake.
• Develop reusable ingestion, transformation, validation, and publishing frameworks for large-scale enterprise datasets.
• Implement medallion or layered lakehouse patterns across raw, curated, and consumption-ready data zones.
• Integrate Azure Databricks with Azure Data Lake Storage Gen2, Azure Data Factory, Azure Key Vault, Azure DevOps, and related Azure services.
• Develop production-grade Databricks notebooks, jobs, workflows, libraries, and cluster configurations.
• Create complex SQL and PySpark transformations, data-quality checks, reconciliation logic, and error-handling routines.
• Optimise Spark workloads, storage layouts, partitioning, file sizes, caching, and query performance while controlling platform cost.
• Implement Delta Lake capabilities such as ACID transactions, schema enforcement, schema evolution, time travel, and merge operations.
• Build automated CI/CD pipelines for code promotion, configuration management, testing, and deployment across environments.
• Apply data security and governance controls using role-based access, secrets management, data masking, auditability, and controlled access patterns.
• Monitor data pipelines, troubleshoot failures, perform root-cause analysis, and deliver sustainable production fixes.
• Partner with Architects, Business Analysts, Data Analysts, Product Owners, and business stakeholders to convert requirements into reliable data products.
• Provide technical leadership, code reviews, engineering standards, and mentoring to other data engineers.