If you are an experienced Data Engineer, SQL Developer or Business Intelligence Developer looking for opportunities in Johannesburg, ADvTECH has advertised a Data Engineer position in Sandton, Gauteng.
The position is a permanent role within the Group IT division and focuses on data engineering, SQL development, business intelligence, data modelling, ETL pipelines, Power BI, data warehouses and data quality.
The vacancy was posted on 23 September 2026, with a stated closing date of 28 September 2026.
This article explains the role, qualifications, experience requirements, technical skills, responsibilities, ATS keywords, FAQs and potential Data Engineer interview questions and answers.
The Data Engineer will be responsible for ingesting, transforming, structuring and maintaining business data so that it can be used by end-users and analytics teams to generate business insights.
A major part of the position involves making sure that data is available from source systems, correctly transformed and accurately loaded into the Data Warehouse.
The role also involves creating:
Data models.
ETL processes.
Data pipelines.
Power BI reports.
Dashboards.
KPI scorecards.
Data dictionaries.
Source-to-target mappings.
The Data Engineer will work with internal and external stakeholders to translate business requirements into technical data solutions.
Main Responsibilities of the Data Engineer
1. Data Analysis and Data Modelling
The successful candidate will design data models according to recommended best practices.
Responsibilities include:
Creating data model designs.
Analysing source data.
Profiling data.
Identifying data-quality issues.
Understanding relationships between datasets.
Identifying patterns and data rules.
Translating business requirements into data requirements.
A strong understanding of data modelling is therefore important for this position.
2. Translate Business Requirements into Data Requirements
The Data Engineer will collaborate with business teams and other technical employees to understand what data is required.
This means the candidate needs to be able to communicate with both technical and non-technical stakeholders.
The process may involve:
Understanding the business requirement.
Identifying the required data.
Identifying the source systems.
Analysing data quality.
Mapping source data to target structures.
Designing the appropriate data model.
Implementing the required data pipeline or reporting solution.
3. Source-to-Target Data Mapping
The role requires the candidate to compile Source to Target Mapping Specifications together with the Business Intelligence Business Analyst.
Source-to-target mapping documents how data moves from its original source into its destination within the data warehouse or another target system.
The mapping can include information such as:
Source table.
Source field.
Target table.
Target field.
Transformation logic.
Data types.
Business rules.
4. Data Quality and Data Accuracy
Data accuracy is a major responsibility of the role.
The Data Engineer will need to identify authoritative or definitive data sources and investigate gaps in source information.
Responsibilities include:
Analysing source data.
Identifying data-quality problems.
Identifying missing information.
Checking relationships between datasets.
Ensuring data is correctly loaded.
Maintaining accurate data within the Data Warehouse.
This requires strong analytical and problem-solving skills.
5. Database Management
The position includes maintaining databases.
The successful candidate may be responsible for:
Restructuring databases.
Cleaning databases.
Deleting old data.
Managing stored procedures.
Maintaining data structures.
Supporting database-related processes.
Strong SQL experience is particularly important because the position requires substantial SQL development experience.
6. Power BI Development
Power BI is a significant part of the advertised role.
The Data Engineer will develop:
Visual reports.
Dashboards.
KPI scorecards.
Power BI reports.
The role also requires the ability to connect Power BI to data sources and import and transform data for business intelligence purposes.
7. DAX
The vacancy specifically requires the candidate to write DAX queries in Power BI.
DAX, or Data Analysis Expressions, is used in Power BI for calculations, measures and data analysis.
Candidates should therefore be prepared for technical interview questions around:
Measures.
Calculated columns.
Filter context.
Row context.
CALCULATE.
Time intelligence.
Relationships.
DAX optimisation.
8. Row-Level Security
The Data Engineer will implement row-level security (RLS) on data.
Row-level security restricts which rows of data a particular user can access.
For example, different users may need access to different departments, regions, schools or business units.
The candidate should therefore understand how security can be applied within the Power BI and data environment.
ETL and Data Pipeline Development
9. Build Data Ingestion and Transformation Processes
The role involves designing and implementing scalable processes for:
Data ingestion.
Data transformation.
ETL.
Data warehouse development.
Pipeline monitoring.
The candidate needs to understand how data moves from source systems into the data warehouse.
10. Synapse Pipelines, Data Factory and SSIS
One of the specific experience requirements is:
3 years of experience with Synapse pipelines / Data Factory / SSIS.
Experience with these technologies can be particularly important for applicants applying for this position.
The candidate should understand concepts such as:
Data ingestion.
Pipeline orchestration.
Transformations.
Scheduling.
Error handling.
Monitoring.
Source-to-target movement.
ETL processes.
11. Data Warehouse Architecture
The Data Engineer will develop work packages that contribute to elements of the data warehouse architecture.
This includes areas such as:
Data ingestion.
ETL.
Data transformation.
Data modelling.
Data quality.
Data warehouse maintenance.
Understanding the relationship between operational systems, staging areas, transformation processes and data warehouses can therefore be useful during the interview.
12. ETL Troubleshooting and Technical Support
The successful candidate will provide technical support for ETL and data warehouse-related issues.
This can include:
Troubleshooting pipeline failures.
Investigating data discrepancies.
Identifying transformation errors.
Resolving loading problems.
Supporting upgrades.
Monitoring ETL processes.
The candidate must also track support issues through to completion.
13. Quality Assurance
The Data Engineer will participate in all aspects of quality assurance.
Data engineering QA can involve checking:
Data completeness.
Data accuracy.
Transformation logic.
Pipeline outputs.
Data types.
Relationships.
Report results.
Business rules.
14. Data Dictionary Development
The position requires the development of a data dictionary.
A data dictionary documents important information about datasets and data fields.
It can include:
Field names.
Definitions.
Data types.
Business descriptions.
Source systems.
Transformation rules.
Relationships.
A good data dictionary helps technical and business teams understand the organisation’s data.
15. DevOps and Task Tracking
The Data Engineer will use DevOps to track:
Tasks.
Effort estimates.
Completed work.
Repositories.
The role also requires the employee to respond to support calls promptly and track issues through to resolution.
Experience working within structured development and task-management environments can therefore be useful.
Continuous Improvement Responsibilities
The role is not limited to maintaining existing systems.
The Data Engineer will also contribute to automated data improvement initiatives under the guidance of the Team Lead.
The candidate is expected to:
Improve technical skills.
Keep up with industry trends.
Research best practices.
Recommend improvements.
Investigate new technologies.
Optimise standard reporting.
This makes continuous learning an important part of the position.
Education Requirements
The advertised minimum education requirement is:
Diploma in IT or a related field.
Applicants should clearly list their IT qualification on their CV.
Experience Requirements
The vacancy specifies the following experience:
SQL Developer
5 years of experience as a SQL Developer.
Candidates should be comfortable working with SQL and databases.
BI Developer
5 years of experience as a Business Intelligence Developer.
Relevant experience may include reporting, dashboards, data analysis and BI solutions.
Data Engineering
3 years of experience in Data Engineering.
Applicants should demonstrate practical experience with data ingestion, transformation, pipelines and data warehouse environments.
Synapse / Data Factory / SSIS
3 years of experience with Synapse pipelines, Data Factory or SSIS.
Applicants should highlight specific projects involving these technologies where applicable.
Key Technical Skills for This Data Engineer Role
Based on the advertised responsibilities, relevant technical skills include:
SQL.
Data engineering.
Data modelling.
Data warehousing.
ETL.
Data pipelines.
Power BI.
DAX.
Microsoft Synapse.
Azure Data Factory.
SSIS.
Data ingestion.
Data transformation.
Data quality.
Source-to-target mapping.
Dimensional modelling.
ERD modelling.
Row-level security.
DevOps.
Database management.
Reporting and dashboards.
ATS Keywords for Your Data Engineer CV
Candidates applying for this position should ensure that their CV accurately reflects relevant experience.
Potential ATS keywords include:
Data Engineer, SQL Developer, BI Developer, Business Intelligence, Data Modelling, Data Warehouse, ETL, Data Pipelines, Data Ingestion, Data Transformation, Power BI, DAX, Microsoft Synapse, Azure Data Factory, SSIS, SQL, Dimensional Modelling, ERD, Source-to-Target Mapping, Data Quality, Data Profiling, Data Dictionary, Row-Level Security, KPI Dashboards, Data Analytics, DevOps, Database Management, Reporting, Data Integration and Data Warehousing.
Avoid adding technologies to your CV simply because they appear in the vacancy. Only list technologies you have actually used.
How to Prepare for the Data Engineer Interview
Expect the interview to potentially combine technical, behavioural and scenario-based questions.
Prepare examples demonstrating:
SQL development.
ETL development.
Data pipeline troubleshooting.
Data modelling.
Power BI development.
DAX.
Data quality.
Data warehouse architecture.
Stakeholder communication.
Problem-solving.
Continuous improvement.
Data Engineer Interview Questions and Answers
1. Tell us about yourself.
Sample answer:
I am a data professional with experience across SQL development, business intelligence and data engineering. My experience includes working with databases, data transformation, ETL processes, data modelling and reporting. I also have experience developing Power BI reports and working with data pipelines. I enjoy solving data problems and turning complex data into reliable information that can support business decisions.
2. Why are you interested in this Data Engineer position?
Sample answer:
I am interested in the role because it combines several areas of data engineering that I enjoy, including SQL, data modelling, ETL, data warehousing and Power BI. I also like the fact that the position involves working with business stakeholders to turn requirements into practical data solutions.
3. What is the difference between ETL and ELT?
Sample answer:
ETL stands for Extract, Transform and Load. Data is extracted from the source, transformed and then loaded into the target system. ELT stands for Extract, Load and Transform, where data is loaded into the target environment first and transformed there. The appropriate approach depends on the architecture, technology and business requirements.
4. How do you ensure data quality?
Sample answer:
I start by profiling the source data to identify issues such as missing values, duplicates, incorrect formats and inconsistent business rules. I then establish appropriate transformation and validation rules. After the data is loaded, I perform reconciliation and quality checks to ensure the target data is complete and accurate.
5. What is a data warehouse?
Sample answer:
A data warehouse is a centralised environment designed to store integrated data from different sources for reporting and analysis. It is structured to support business intelligence and analytical workloads rather than primarily supporting day-to-day transactional processing.
6. What is dimensional modelling?
Sample answer:
Dimensional modelling structures data around fact and dimension tables to make analytical queries and reporting easier. Fact tables generally contain measurable business events, while dimension tables provide descriptive information used to analyse those events.
7. What is the difference between a fact table and a dimension table?
Sample answer:
A fact table generally contains measurable business events such as transactions, sales or enrolments. Dimension tables contain descriptive attributes used to analyse those events, such as customer, product, location or date information.
8. What is a star schema?
Sample answer:
A star schema is a dimensional modelling structure where a central fact table is connected to surrounding dimension tables. It is commonly used in data warehouses because the structure is relatively straightforward for analytical queries and reporting.
9. How would you troubleshoot a failed ETL pipeline?
Sample answer:
I would first identify where the pipeline failed and review the error message and execution logs. I would check the source connection, authentication, input data, transformation logic and target destination. I would then isolate the root cause, correct the issue, rerun the appropriate process and validate the output before closing the incident.
10. What is Power BI used for?
Sample answer:
Power BI is a business intelligence platform used to connect to data sources, transform and model data, create visualisations and develop interactive reports and dashboards. It can help users analyse data and monitor KPIs.
11. What is DAX?
Sample answer:
DAX stands for Data Analysis Expressions. It is a formula language used in Power BI and related Microsoft data tools to create calculations, measures and other analytical expressions.
12. What is the difference between a calculated column and a measure in Power BI?
Sample answer:
A calculated column is calculated for each row in a table and becomes part of the model. A measure is calculated dynamically based on the filter context of the report or visual. Measures are generally preferred for aggregations and analytical calculations because they are evaluated when needed.
13. Explain filter context in DAX.
Sample answer:
Filter context is the set of filters that determines which data is considered when a DAX expression is evaluated. It can come from report filters, slicers, visual selections, relationships and other DAX expressions.
14. What is row-level security?
Sample answer:
Row-level security restricts the data that individual users can access based on defined rules. For example, users from different business units could be restricted to seeing only the records relevant to their assigned business unit.
15. How would you implement row-level security in Power BI?
Sample answer:
I would first define the business security requirements and determine which users should access which data. I would then create appropriate roles and filtering rules in the model, establish the required user-to-business-unit relationships and test the roles with representative accounts before deployment.
16. What is Azure Data Factory?
Sample answer:
Azure Data Factory is a cloud-based data integration service that can be used to create and orchestrate data movement and transformation pipelines between different data sources and destinations.
17. What is SSIS?
Sample answer:
SQL Server Integration Services is Microsoft’s platform for building data integration and ETL workflows. It can be used to extract data from different sources, transform it and load it into target systems such as databases or data warehouses.
18. How do you optimise a slow SQL query?
Sample answer:
I would first analyse the execution plan and identify the expensive operations. I would check joins, filters, indexes, table scans, unnecessary columns and subqueries. Depending on the issue, I might optimise the query structure, add or adjust indexes, reduce unnecessary data processing or review the underlying database design.
19. What is source-to-target mapping?
Sample answer:
Source-to-target mapping documents how fields from a source system correspond to fields in the target system. It normally includes transformation rules and business logic so that developers can implement the data integration consistently.
20. How do you handle duplicate data?
Sample answer:
First, I determine why the duplicates exist and whether they are genuine duplicates or legitimate repeated records. I would then define an appropriate business rule for identifying duplicates and apply the appropriate transformation or cleansing process. I would also validate the results to make sure legitimate records have not been removed.
Scenario-Based Data Engineer Interview Questions
Scenario 1: A Power BI dashboard is showing incorrect figures.
Sample answer:
I would first determine whether the issue originates in the report, data model, transformation process or source data. I would validate the underlying data against the source system, check relationships and filters, review DAX calculations and confirm that the ETL process completed successfully. Once I identify the root cause, I would correct it and validate the results before releasing the updated report.
Scenario 2: An overnight data pipeline fails.
Sample answer:
I would review the pipeline execution history and error logs to identify the failed activity. I would check whether the issue relates to connectivity, source data, transformation logic, authentication or the target system. After resolving the root cause, I would rerun the appropriate process and validate the loaded data.
Scenario 3: Business users say that a dashboard is too slow.
Sample answer:
I would analyse the report and model performance to identify the bottleneck. I would review the data model, relationships, DAX measures, data volume and visual queries. Depending on the cause, I might optimise the model, improve DAX calculations, reduce unnecessary data or change the data-refresh approach.
Scenario 4: Two departments provide different figures for the same KPI.
Sample answer:
I would investigate the definition of the KPI and identify the source systems and calculation rules used by each department. I would compare the underlying data and business definitions and establish the authoritative source or agreed calculation method. I would then document the agreed definition so that future reporting is consistent.
Behavioural Interview Questions
How do you deal with competing priorities?
I assess the urgency and business impact of each task, clarify deadlines where necessary and organise my workload accordingly. I also communicate early if priorities conflict rather than allowing deadlines to be missed without explanation.
How do you explain technical information to non-technical stakeholders?
I avoid unnecessary technical terminology and focus on the business outcome. I use examples, simple explanations and visualisations where appropriate. I also confirm that the stakeholder’s requirement has been understood before implementing the solution.
Tell us about a difficult data problem you solved.
I would explain the situation, the data problem, how I investigated the root cause, the technical solution I implemented and the measurable result. I would focus on what I personally contributed and what I learned from the experience.
How do you keep your technical skills up to date?
I follow developments in data engineering and business intelligence, practise new technologies through relevant projects and research best practices. I also learn from technical documentation, colleagues and real-world implementation challenges.
Questions to Ask the Interviewer
At the end of the interview, you could ask:
What does the current data warehouse architecture look like?
Which Azure and Microsoft data technologies are currently used?
What are the main data engineering priorities for the first six months?
How is data quality currently monitored?
How does the Data Engineering team work with the BI and Business Analyst teams?
What are the main challenges the successful candidate will be expected to address?
Frequently Asked Questions
Where is the Data Engineer position located?
The position is based in Sandton, Gauteng.
Is the position permanent?
Yes. The vacancy is advertised as permanent.
When was the vacancy posted?
It was posted on 23 September 2026.
What is the closing date?
The stated closing date is 28 September 2026.
What qualification is required?
The advertised education requirement is a Diploma in IT or a related field.
How much SQL experience is required?
The vacancy specifies 5 years of experience as a SQL Developer.
How much BI experience is required?
The requirement is 5 years of experience as a BI Developer.
How much Data Engineering experience is required?
The vacancy specifies 3 years of Data Engineering experience.
Is Power BI experience required?
The responsibilities specifically include developing Power BI reports, dashboards and KPI scorecards, as well as writing DAX queries.
Is DAX required?
Yes. The vacancy specifically states that the successful candidate will write DAX queries in Power BI.
Is ETL experience required?
Yes. ETL and pipeline development are major responsibilities of the position.
Which pipeline technologies are mentioned?
The vacancy specifically mentions Synapse pipelines, Data Factory and SSIS, with three years of experience required.
Does the job involve data modelling?
Yes. Data modelling is a core part of the position, including logical, physical, ERD and dimensional models.
Does the role involve data warehousing?
Yes. The Data Engineer will work with the Data Warehouse, including data loading, ETL, architecture and maintenance.
Does the role involve DevOps?
Yes. The position requires using DevOps to track tasks, effort estimates, completed work and repositories.
Final Application Tips
This is a technically focused role, so your CV should make your relevant experience immediately visible.
Prioritise:
5 years SQL development experience
5 years BI development experience
3 years Data Engineering experience
3 years Synapse/Data Factory/SSIS experience
Power BI.
DAX.
Data modelling.
ETL.
Data pipelines.
Data warehousing.
Data quality.
Source-to-target mapping.
Data dictionaries.
Row-level security.
DevOps.
Database development.
Where possible, describe achievements using measurable results—for example, improvements in pipeline reliability, report performance, data quality, processing time or reporting efficiency.
For the interview, be ready to explain your technical decisions rather than simply naming technologies. Interviewers may want to understand how you designed a pipeline, solved a data-quality problem, optimised SQL, developed a Power BI model or handled a production failure.
Nomcebo Yaka is a South African opportunity curator and digital platform builder dedicated to helping young people access employment and skills development opportunities. Through her social media presence and website, she shares verified job openings, learnerships, and career resources, making it easier for job seekers to stay informed and apply on time.
Driven by a passion for community upliftment, she actively connects unemployed individuals with potential employers and encourages proactive job searching. Her work focuses on bridging the gap between opportunity and access, particularly for youth across South Africa.
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