Data Analyst

Location: Centurion, Gauteng (provisional – to be confirmed)

Positions Available: 5

Salary: Market-related

Employment Type: To be confirmed

Job Overview

We are seeking experienced, analytical and technically proficient

Data Analysts to support enterprise data analysis, business intelligence, reporting, data quality management and data-driven decision-making within complex organisational environments.

The successful candidates will be responsible for collecting, extracting, transforming, analysing and interpreting large volumes of structured and semi-structured data to identify trends, evaluate business performance and deliver meaningful insights to business and technical stakeholders.

This role requires strong hands-on expertise in SQL, data analysis, data visualisation, business intelligence tools, statistical analysis and data quality assessment .

The ideal candidates will have proven experience working with enterprise databases, developing analytical reports and dashboards, investigating data discrepancies, identifying business trends and translating complex datasets into clear, actionable recommendations.

Candidates should demonstrate the ability to work independently with data from multiple systems, develop reliable reporting solutions, collaborate with data engineering and business intelligence teams, and communicate analytical findings to stakeholders at different organisational levels.

The role also requires an understanding of data governance, information security, data privacy and enterprise data management principles.

This is a specialist Data Analyst opportunity requiring demonstrated hands-on technical data analysis experience. General administrative reporting, basic Excel usage or data capturing experience alone will not be sufficient.

Key Responsibilities

Data Collection, Extraction and Preparation

• Extract data from enterprise databases, applications and information systems.
• Develop and execute SQL queries to retrieve relevant business and operational data.
• Collect and consolidate data from multiple internal and external sources.
• Analyse source data structures and identify relevant datasets.
• Perform data cleansing, transformation and standardisation activities.
• Identify missing, duplicate, inconsistent or inaccurate data.
• Prepare structured datasets for reporting and analysis.
• Validate extracted data against source systems.
• Support the integration of data from different business applications.
• Maintain accurate documentation of data sources, extraction methods and transformation rules.

Data Analysis and Interpretation

• Analyse large and complex datasets to identify trends, patterns and relationships.
• Perform descriptive, diagnostic and exploratory data analysis.
• Investigate business performance trends and operational variances.
• Identify anomalies, outliers and unexpected data behaviour.
• Conduct comparative and historical data analysis.
• Develop analytical models to support business decision-making.
• Interpret analytical results and explain their business implications.
• Identify opportunities for operational efficiency and performance improvement.
• Conduct root cause analysis of data-related business issues.
• Translate analytical findings into practical recommendations.

SQL Development and Database Analysis

• Develop complex SQL queries for data extraction and analysis.
• Use joins, aggregations, subqueries and Common Table Expressions (CTEs).
• Apply window functions and analytical SQL techniques.
• Analyse relational database structures and relationships.
• Develop reusable queries and analytical datasets.
• Investigate data discrepancies across databases and systems.
• Support query optimisation and performance improvement.
• Work with database administrators and data engineers to resolve data issues.
• Validate data accuracy and consistency across reporting environments.
• Document SQL logic and data transformation requirements.

Business Intelligence and Data Visualisation

• Develop interactive dashboards and business intelligence reports.
• Translate business requirements into effective data visualisations.
• Build reports using Power BI, Tableau or equivalent enterprise BI platforms.
• Define and calculate business performance measures.
• Create dashboards to monitor operational and strategic KPIs.
• Develop drill-down reports and interactive analytical views.
• Ensure reports are accurate, understandable and fit for purpose.
• Maintain existing dashboards and reporting solutions.
• Improve report usability, performance and consistency.
• Present data insights using appropriate visualisation techniques.

Power BI and Reporting Development

• Develop and maintain Power BI reports and dashboards.
• Connect Power BI to enterprise databases and approved data sources.
• Prepare and transform data using Power Query.
• Develop data models and relationships to support reporting.
• Create calculated measures and business metrics using DAX.
• Implement appropriate report filters, slicers and navigation.
• Validate Power BI calculations against source data.
• Support scheduled refreshes and reporting reliability.
• Apply appropriate report-level and dataset security controls.
• Optimise report performance and user experience.

Data Quality and Validation

• Conduct data profiling and quality assessments.
• Identify data completeness, consistency, validity and accuracy issues.
• Investigate discrepancies between source systems and reporting outputs.
• Develop data validation and reconciliation procedures.
• Support the implementation of data quality rules and controls.
• Monitor recurring data quality problems.
• Collaborate with data owners and technical teams to resolve issues.
• Document data quality findings and corrective actions.
• Support data quality improvement initiatives.
• Ensure analytical outputs are based on reliable and appropriately validated information.

Statistical Analysis and Analytical Techniques

• Apply appropriate statistical methods to analyse business datasets.
• Conduct trend, variance and correlation analysis.
• Use descriptive statistics to summarise data.
• Support forecasting and predictive analysis where required.
• Perform segmentation and comparative analysis.
• Identify statistically meaningful patterns and anomalies.
• Evaluate analytical assumptions and limitations.
• Use Python, R or equivalent analytical tools where applicable.
• Interpret statistical findings in a business context.
• Communicate analytical conclusions clearly and accurately.

Business Requirements And Stakeholder Engagement

• Engage with business stakeholders to understand analytical and reporting requirements.
• Translate business questions into structured data analysis activities.
• Facilitate discussions regarding KPIs, reporting definitions and data requirements.
• Clarify business rules affecting analytical calculations.
• Present findings to operational managers and senior stakeholders.
• Explain technical analysis in clear business language.
• Collaborate with business analysts, process specialists and business intelligence teams.
• Manage competing reporting and analytical priorities.
• Support data-driven business planning and performance reviews.
• Recommend improvements to reporting and analytical processes.

Enterprise Data Integration and Data Warehousing

• Work with data engineers and database specialists to understand enterprise data structures.
• Analyse data sourced from data warehouses, data marts and operational systems.
• Support data mapping and transformation requirements.
• Understand ETL and ELT processes and their impact on analytical outputs.
• Validate datasets produced by data integration pipelines.
• Identify data lineage and source-to-target relationships.
• Support reporting requirements for new data integrations.
• Assist with data reconciliation during migration initiatives.
• Evaluate the impact of changes to data structures on reports.
• Contribute to improvements in analytical data availability.

Data Governance, Privacy and Security

• Apply organisational data governance policies and standards.
• Handle confidential and sensitive information appropriately.
• Ensure data analysis activities comply with applicable information security requirements.
• Support data classification and access control requirements.
• Apply relevant privacy and data protection principles, including POPIA where applicable.
• Maintain appropriate controls over analytical datasets and reports.
• Document data definitions and reporting business rules.
• Support data ownership and stewardship practices.
• Identify potential data governance risks.
• Promote responsible and ethical use of organisational data.

Reporting Automation and Continuous Improvement

• Identify opportunities to automate recurring reports and analytical processes.
• Reduce manual data extraction and spreadsheet-based reporting.
• Develop reusable SQL queries, scripts and reporting templates.
• Improve the efficiency of data preparation and reporting workflows.
• Support automation using Power BI, Power Query, Python or other approved tools.
• Evaluate opportunities to standardise enterprise reporting.
• Monitor report performance and data refresh reliability.
• Recommend improvements to analytical methods and tools.
• Support the implementation of self-service business intelligence capabilities.
• Contribute to continuous improvement of enterprise data analytics practices.

Documentation and Quality Assurance

• Maintain documentation of analytical methods, data sources and reporting logic.
• Document business definitions, calculations and KPI methodologies.
• Prepare data dictionaries and analytical specifications.
• Validate reports before publication or distribution.
• Conduct quality assurance checks on dashboards and analytical outputs.
• Maintain appropriate version control for queries and scripts.
• Support peer reviews and analytical testing.
• Document assumptions, limitations and known data quality issues.
• Ensure analytical outputs are reproducible and traceable.
• Follow approved enterprise reporting and documentation standards.

Minimum Requirements

• Relevant diploma or degree in Data Analytics, Data Science, Statistics, Mathematics, Computer Science, Information Systems, Information Technology, Business Intelligence or a related discipline.
• Typically 3–5+ years of relevant professional experience in data analysis, business intelligence, reporting analytics or a closely related technical data role.
• Proven hands-on SQL experience involving data extraction, querying, transformation and analysis – essential.
• Demonstrated experience analysing complex datasets and producing meaningful business insights.
• Strong practical experience using Power BI, Tableau or equivalent enterprise business intelligence tools.
• Experience developing dashboards, management reports and KPI reporting solutions.
• Advanced Microsoft Excel skills, including analytical functions and data manipulation.
• Experience working with relational databases and structured enterprise datasets.
• Practical knowledge of data cleansing, profiling, validation and reconciliation.
• Experience translating business requirements into analytical outputs.
• Strong understanding of data modelling and reporting data structures.
• Familiarity with Power Query and DAX would be advantageous.
• Experience using Python or R for data analysis would be beneficial.
• Understanding of statistical analysis and analytical methodologies.
• Familiarity with ETL/ELT processes and data warehouse concepts.
• Understanding of data governance, privacy and information security principles.
• Strong analytical reasoning and problem-solving abilities.
• Excellent communication and stakeholder engagement skills.
• Ability to work independently and manage multiple analytical assignments.
• Experience within large or complex enterprise environments would be advantageous.

Technical Skills and Competencies

SQL and Database Technologies

• SQL
• Microsoft SQL Server
• T-SQL
• PostgreSQL
• Oracle Database
• MySQL
• IBM Db2
• SQL joins and aggregations
• Common Table Expressions (CTEs)
• Window functions
• Subqueries
• Stored procedure fundamentals
• Query optimisation fundamentals
• Database relationships
• Relational data analysis
• Data extraction and validation

Business Intelligence and Reporting

• Microsoft Power BI
• Tableau
• Qlik Sense
• QlikView
• Microsoft Excel
• Power Query
• DAX
• Power BI Desktop
• Power BI Service
• Dashboard development
• KPI reporting
• Interactive visualisation
• Self-service business intelligence
• Management reporting
• Report performance optimisation

Data Analysis and Statistical Techniques

• Exploratory data analysis
• Descriptive statistics
• Diagnostic analytics
• Trend analysis
• Variance analysis
• Correlation analysis
• Data segmentation
• Comparative analysis
• Root cause analysis
• Anomaly detection
• Forecasting fundamentals
• Statistical interpretation
• Analytical modelling
• Business performance analysis

Python and Analytical Programming

Experience with relevant analytical programming tools would be advantageous:

• Python
• Pandas
• NumPy
• Matplotlib
• Jupyter Notebook
• R
• RStudio
• Data manipulation scripts
• Data validation scripts
• Reporting automation
• Exploratory data analysis using code

Data Preparation and Transformation

• Data extraction
• Data cleansing
• Data transformation
• Data standardisation
• Data profiling
• Data reconciliation
• Data quality assessment
• Data mapping
• Data aggregation
• Dataset preparation
• Data validation
• Missing data analysis
• Duplicate data identification
• Data consistency checks

Data Warehousing and Enterprise Data Platforms

• Data warehouse concepts
• Data marts
• ETL and ELT fundamentals
• Dimensional modelling
• Star schemas
• Fact and dimension tables
• Data pipelines
• Microsoft Azure data services
• Azure SQL Database
• Azure Data Factory awareness
• Microsoft Fabric awareness
• Snowflake
• Databricks fundamentals
• Enterprise data integration

Data Governance and Information Management

• Data governance principles
• Data quality management
• Data classification
• Data privacy
• POPIA awareness
• Data access controls
• Data ownership
• Data stewardship
• Data dictionaries
• Metadata documentation
• Data lineage fundamentals
• Responsible data handling

Reporting And Analytical Requirements

• Business requirements gathering
• Reporting specifications
• KPI definition
• Business rules documentation
• Analytical requirements analysis
• Data source identification
• Report validation
• Stakeholder presentations
• Business performance reporting
• Management information analysis
• Reporting quality assurance

Reporting Automation and Productivity

• Power Query automation
• Power BI scheduled refresh
• SQL reporting scripts
• Python automation
• Microsoft Excel Power Pivot
• Excel PivotTables
• Advanced Excel formulas
• Data transformation workflows
• Reusable reporting templates
• Version control fundamentals
• Git awareness
• Analytical process improvement

Relevant Certifications (Advantageous)

One or more of the following certifications would be beneficial:

• Microsoft Certified: Power BI Data Analyst Associate (PL-300)
• Microsoft Certified: Fabric Analytics Engineer Associate (DP-600)
• Microsoft Certified: Azure Data Fundamentals (DP-900)
• Microsoft Certified: Azure Enterprise Data Analyst Associate, where previously obtained
• Tableau Certified Data Analyst
• Relevant SQL or database certifications
• IBM Data Analyst Professional Certificate
• Google Data Analytics Professional Certificate
• Relevant Python or statistical analysis certifications
• Relevant data governance or business intelligence qualifications
• Other recognised data analytics, reporting or business intelligence certifications

Key Personal Attributes

• Exceptional analytical and problem-solving abilities.
• Strong numerical reasoning and attention to detail.
• Ability to interpret complex datasets accurately.
• Strong technical curiosity and investigative skills.
• Excellent written and verbal communication abilities.
• Ability to explain analytical findings to non-technical stakeholders.
• Strong business acumen and understanding of performance measures.
• Structured and methodical approach to data analysis.
• High standards of data accuracy and reporting quality.
• Ability to work independently and manage competing priorities.
• Strong stakeholder engagement and collaboration skills.
• Commitment to continuous learning and technical development.
• Ability to identify meaningful insights rather than simply produce reports.
• Strong understanding of data confidentiality and responsible information handling.
• High levels of professionalism, accountability and integrity.