Data Engineering Instructor

AmaliTech Rwanda is seeking a highly motivated and experienced Data Engineering Instructor to support both
the Training Center and Service Center functions. The successful candidate will play a key role in the AmaliTech
Apprenticeship Program (AAP) a quarterly talent development initiative designed to equip aspiring technology
professionals with industry-relevant skills and practical experience.

In this role the instructor will primarily support the Data Engineering track by delivering technical training
mentoring trainees assessing learning progress and contributing to the development of industry-ready data
engineering talent. The instructor will also collaborate with internal teams including the Data Engineering team in Ghana to ensure alignment with industry standards and best practices.

Qualifications :

• Minimum of 2 years of professional experience in Data Engineering or a related field.
• Strong proficiency in Python (Pandas NumPy PySpark) and SQL.
• Hands-on experience with:
  • Apache Airflow
  • Apache Spark
  • Apache Kafka
• Practical knowledge of AWS data services including:
  •  S3 Glue Redshift Kinesis
  •  Lambda DynamoDB Step Functions Athena
•  Experience with Microsoft Power BI including:
  • Report and dashboard development
  • DAX Power Query Data modeling
• Familiarity with Microsoft Fabric including:
  • Lakehouse Data Factory Spark Notebooks OneLake
• Understanding of data engineering principles:
  • Data modeling and schema design ETL/ELT patterns
  • Lakehouse architecture Delta Lake Change Data Capture (CDC)
  • Schema evolution Data governance principles
•  Experience with CI/CD pipelines and GitHub Actions for data workflows.
•  Previous experience teaching mentoring or facilitating technical learning programs is an added advantage.

Nice to Have

• AWS Certifications (e.g. AWS Certified Data Engineer Associate Solutions Architect).
• Microsoft Certifications (e.g. PL-300 Power BI DP-700 Fabric Analytics Engineer).
• Exposure to Data Science or Machine Learning concepts including scikit-learn and model evaluation techniques.

Key Responsibilities 

• Deliver engaging technical training and mentorship in Data Engineering covering Python SQL Apache Spark Kafka Airflow and modern data architectures.
• Guide trainees in designing building and managing scalable ETL/ELT pipelines and cloud-based data solutions using AWS and Microsoft Fabric technologies.
• Support learners in data visualization and business intelligence development using Power BI including dashboard creation DAX Power Query and data modeling.
• Assess grade and validate trainee projects assignments and technical assessments to ensure industry-ready competency levels.
• Mentor trainees on best practices in data governance CI/CD workflows GitHub Actions Delta Lake and lakehouse architectures.
• Collaborate with internal technical teams to continuously improve curriculum content training delivery and learning outcomes.
• Contribute to fostering a hands-on project-based learning environment that encourages innovation problem-solving and teamwork.

Soft Skills

• Strong ability to explain complex technical concepts to learners with varying levels of experience.
• Excellent facilitation communication and classroom management skills.
• Comfortable working in a hands-on project-based learning environment.
• Fast learner with strong adaptability and problem-solving abilities.
• Flexible and collaborative with the ability to work effectively with cross-functional and international teams
• including the Data Engineering team in Ghana.
• Strong organizational and time-management skills.

Additional Information :

Recruitment Processes

• Application Review
• Take-home Assessments
• Technical Interviews
• Culture Fit Interview
• Offer Stage.

Potential applicants are encouraged to contact the Recruitment Team to address concerns or clarification. 

• Email:
• Phone:

Application Guideline:

• Click Im Interested to apply.
• In the application form remember to choose the specialisation youre applying for.

Remote Work :

No

Employment Type :

Full-time