Data Analyst

Full Time
  • September 14, 2026
  • Employment Info

    JOB   DETAILS

    Requirements
    • 3+ years working on data platform implementation, migration, or modernisation projects
    • Tertiary qualifications in Computer Science, Engineering, or a related technical field
    • Demonstrated ability to take ownership of projects, collaborate cross-functionally, and work independently in dynamic environments
    • Strong SQL skills — complex queries, reconciliation logic, row counts, sum checks, and data profiling
    • Good working knowledge of Python for validation scripts, automated reconciliation, and data profiling
    • Hands-on experience with dbt — dbt tests, schema tests, and custom data quality checks
    • Experience validating data across platform migrations — source-to-target reconciliation, regression testing, and cutover validation
    • Experience with major cloud data warehouse or lakehouse platform (e.g. Snowflake, Databricks, BigQuery)
    • Good understanding of ELT/ETL concepts and medallion or layered architectures
    • Experience with Tableau, Power BI, or similar visualisation tools
    • Ability to work cross-functionally with engineers and business stakeholders
    Responsibilities
    • Engage with business stakeholders to gather and translate data requirements into testable acceptance criteria and quality checks within the Data Squad
    • Design and build scalable pipelines and data models that support data quality, reconciliation, and trusted insight generation within the Data Squad
    • Own and evolve the data stack with a focus on data quality and testing practices, supporting platform delivery (e.g. dbt, SQL, Python, orchestration, reconciliation, validation, observability)
    • Ensure high standards of data accuracy, integrity, and reliability — especially during platform migration and implementation in a Medallion architecture
    • Develop and maintain robust documentation, schema management, and lineage tracking to ensure transparency and traceability
    • Forming a deep understanding of our data processes and helping to improve them through implementing best practices
    • Staying on top of any failures and issues in our data systems, troubleshooting, proposing improvements and implementing fixes

     

     

     

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