Mid level data analytics engineer

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MID LEVEL DATA ANALYTICS ENGINEER Role Overview

The Data Analytics Engineer is responsible for transforming data into meaningful insights that support business decision-making. The role spans the full analytics lifecycle, including requirements gathering, source data analysis, data modelling, data engineering, report development, dashboard visualisation, and ongoing optimisation of business intelligence solutions. The successful candidate will possess strong technical capability across data preparation, warehousing, transformation, and visualisation, with a particular focus on Microsoft Power BI Analytics Solutions. The role requires the ability to engage with business stakeholders, translate requirements into technical solutions, and deliver high-quality, actionable insights.

Required Qualifications:

Tertiary qualification (degree, diploma, or certificate) in a relevant field (e.g., Computer Science, IT, Informatics, or Data Analytics)

Experience & Knowledge Requirements Technical Skills Microsoft Power BI (Essential) Tableau (Advantageous) Microsoft SQL Server Data Warehousing Methodologies and Best Practices Data Modelling (Star Schema, Dimensional Modelling) Data Consolidation and Integration Data Cleansing and Transformation ETL Development Database Design and Optimisation Dashboard and Report Development DAX (Data Analysis Expressions) Power Query (M Language) Azure Data Fundamentals and Azure Data Services Exposure Advantageous Experience Azure Data Factory Azure Synapse Analytics Azure Data Lake Microsoft Fabric Databricks AWS and AWS Redshift Oracle Databases SSIS, SSAS and SSRS Python, R or Alteryx AI and Machine Learning exposure Big Data technologies such as Hadoop, Hive and Spark RPA tools such as Ui Path Agile delivery methodologies Azure Dev Ops practices and tooling Key Responsibilities Reporting, Analytics & Visualisation Design, develop, implement and maintain Power BI reports, dashboards and semantic models. Develop interactive visualisations and KPI dashboards to support business decision-making. Create and maintain Power BI datasets, dataflows and workspaces. Develop and optimise DAX measures and calculations. Translate business requirements into meaningful analytical insights. Monitor and improve dashboard performance and usability. Support business users with reporting and self-service analytics requirements. Data Engineering & Analytics Analyse source systems and business requirements. Perform data sourcing, profiling, cleansing, transformation and validation. Design and develop data models for reporting and analytics. Design, develop and maintain data warehouse and reporting solutions. Develop ETL and data integration processes. Ensure data quality, consistency and governance standards. Identify opportunities for process automation and optimisation. Business Engagement Gather and document business requirements. Facilitate stakeholder workshops and requirements sessions. Translate business challenges into scalable Power BI and analytics solutions. Collaborate with technical teams and business stakeholders to deliver value. Build and maintain strong client and team relationships. Quality Assurance & Continuous Improvement Conduct thorough testing and validation of reporting and analytics solutions. Document technical specifications, designs and operating procedures. Provide support and troubleshooting for Power BI environments. Recommend and implement process improvements. Research emerging Microsoft Data & AI technologies and analytics best practices. Required Technology Stack Essential Power BI DAX Power Query SQL Server Data Modelling ETL Development Data Warehousing Azure Fundamentals Preferred Microsoft Fabric Azure Data Factory Azure Synapse Azure Data Lake Databricks Python AI & Machine Learning Azure Dev Ops