Hybrid Data Engineer — Fabric, Pipelines & Power BI

APM

North Sydney

Hybrid

AUD 120,000 - 160,000

Full time

6 days ago
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Job summary

APM is seeking a Data Engineer to join our growing data team to deliver modern, scalable enterprise data solutions using Microsoft Fabric and Power BI. You will design and build data pipelines, models and reporting that transform complex data into actionable insights.

Collaborate with architects, analysts and stakeholders to ensure secure, governed data platforms, optimise dashboards and enable data-driven decision making across the organisation. Hybrid work available.

Qualifications

  • Experience with Microsoft Fabric and modern cloud data platforms.
  • Strong SQL and data modelling skills demonstrating ability to design scalable data models.
  • Experience building and supporting data pipelines and integration solutions.
  • Knowledge of Lakehouse, data warehousing and analytics concepts; familiarity with Power BI and DAX.

Responsibilities

  • Design, build and support data solutions using Microsoft Fabric and related tools.
  • Develop and maintain batch and near real-time data pipelines from multiple sources.
  • Create data transformations using SQL, Python and PySpark.
  • Implement Medallion Architecture across Bronze, Silver and Gold layers.
  • Support development and optimisation of Power BI datasets, dashboards and reporting solutions.
  • Monitor and improve data quality, performance, reliability and security.
  • Maintain data lineage, documentation, mappings and runbooks.
  • Assist with data platform modernisation and migration initiatives.
  • Provide operational support, troubleshooting and root cause analysis for data platform issues.
  • Collaborate with stakeholders to translate requirements into practical data solutions.

Skills

Microsoft Fabric
SQL
Data modelling
Data pipelines
Lakehouse
Data warehousing
Power BI
DAX
Data governance
Troubleshooting
Prioritisation

Tools

Azure Data Factory
Microsoft SQL Server
Python

Job description

Build the Data Foundations That Drive Better Decisions

We're seeking a Data Engineer to join our growing data team and help deliver modern, scalable and secure enterprise data solutions. You'll play a key role in building and supporting our Microsoft Fabric environment, enabling reliable reporting, analytics and data-driven decision-making across the organisation.

Working alongside senior engineers, architects, analysts and business stakeholders, you'll design and develop data pipelines, data models and reporting solutions that transform complex data into meaningful business insights.

About the Role

As a Data Engineer, you'll be responsible for building, integrating and supporting enterprise data and analytics solutions using Microsoft Fabric and Power BI. You'll work across data ingestion, transformation, modelling and reporting while ensuring solutions are secure, governed and scalable.

Key responsibilities include:

  • Design, build and support data solutions using Microsoft Fabric, including OneLake, Lakehouse, Warehouse, Data Factory, Dataflows Gen2 and Semantic Models
  • Develop and maintain batch and near real-time data pipelines from enterprise applications, APIs, databases, SaaS platforms and cloud storage
  • Create data transformations using SQL, Python and PySpark
  • Implement Medallion Architecture across Bronze, Silver and Gold data layers
  • Support the development and optimisation of Power BI datasets, dashboards and reporting solutions
  • Monitor and improve data quality, performance, reliability and security
  • Maintain data lineage, documentation, mappings, runbooks and technical standards
  • Assist with data platform modernisation, migration and continuous improvement initiatives
  • Provide operational support, troubleshooting and root cause analysis for data platform issues
  • Collaborate with business stakeholders to translate requirements into practical data solutions

About You

We're looking for a technically capable Data Engineer who enjoys solving data challenges and collaborating with stakeholders to deliver meaningful outcomes.

Essential Experience

  • Experience working with Microsoft Fabric and modern cloud-based data platforms
  • Strong skills in SQL and data modelling
  • Experience building and supporting data pipelines and integration solutions
  • Knowledge of Lakehouse, data warehousing and analytics concepts
  • Experience using Python and/or PySpark for data transformation and automation
  • Exposure to Power BI, semantic models, DAX and reporting solutions
  • Understanding of data governance, security, access controls and compliance requirements
  • Strong analytical, troubleshooting and problem-solving skills
  • Ability to manage multiple priorities and work effectively within project and operational environments

Highly Regarded

  • Experience with Microsoft SQL Server, Azure SQL and Azure Data Factory
  • Exposure to enterprise data platform projects or cloud transformation initiatives
  • Microsoft Data, Azure or Fabric certifications
  • Knowledge of database performance tuning, monitoring and optimisation
  • Experience with automation and Infrastructure-as-Code practices

What You'll Bring

  • Strong communication and stakeholder engagement skills
  • A collaborative approach and willingness to learn
  • Attention to detail and commitment to quality
  • Strong documentation and organisational skills
  • Passion for data, analytics and continuous improvement

Why Join Us?

  • Work with leading Microsoft Fabric and cloud technologies
  • Be part of a modern data and analytics transformation journey
  • Hybrid and flexible work arrangements
  • Supportive and collaborative team environment
  • Opportunities to grow your technical expertise and career
  • Work on impactful projects that directly influence business outcomes

If you're passionate about data engineering and want to help build a modern, scalable data platform that supports analytics, reporting and future AI capabilities, we'd love to hear from you.

Apply today and help shape the future of data and insights.

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