Data Engineer / Junior Data EngineerRole Description

We are seeking a technical, analytical, and detail-oriented Data Engineer / Junior Data Engineer to join our data platform and business intelligence team. In this entry-to-early-career role, you will play a crucial part in building, optimizing, and maintaining scalable data pipelines, architecture, and data infrastructure across the organization. Working closely with Senior Data Engineers, Data Scientists, Analytics Engineers, and Software Developers, you will assist in ingesting raw data from disparate operational systems, transforming it into structured formats, and loading it into centralized data warehouses to support downstream analytics and reporting.

Responsibilities include building ELT/ETL pipelines, monitoring pipeline reliability and data quality, optimizing database query performance, modeling analytics schemas, and documenting data flows. Success in this position requires a strong foundation in SQL, solid programming skills, familiarity with modern data stack tools, high attention to detail, and a proactive problem-solving mindset.

Qualifications

- Education: Bachelor's degree or Diploma in Data Engineering, Computer Science, Information Technology, Software Engineering, Applied Mathematics, or a related quantitative field.

- Advanced SQL &
- Database Systems:

Advanced proficiency in writing complex SQL queries (window functions, CTEs, indexing, query optimization) and hands-on experience working with relational (e.g., PostgreSQL, MySQL, SQL Server) and columnar/cloud databases (e.g., Snowflake, BigQuery, Redshift).

- Programming &

- Scripting:





Strong programming capability in Python or Scala for data manipulation, building automation scripts, interacting with REST APIs, and utilizing data libraries (e.g., Pandas, NumPy).

- ETL/ELT Pipeline Development: Practical exposure to designing, building, and maintaining automated data integration pipelines using up-to-date orchestration tools (e.g., Apache Airflow, Prefect, Dagster, or dbt).

- Data Modeling Principles: Foundational understanding of relational and dimensional data modeling techniques (e.g., Star Schema, Snowflake Schema, Data Vault) and data normalization concepts.

- Big Data &

- Distributed Computing Exposure:

Conceptual knowledge or hands-on experience with distributed data processing frameworks (e.g., Apache Spark, PySpark, or Apache Kafka) for handling large-scale datasets.

- Version Control &

- DevOps Practices:

Proficiency with Git/GitHub for code versioning, pull requests, and familiarity with CI/CD deployment pipelines for data infrastructure.

- Data Quality &

- Governance:

Commitment to implementing automated data validation checks, monitoring pipeline failures, ensuring data integrity, and adhering to data privacy and compliance standards.

- Cloud Infrastructure Basics: Practical familiarity with core cloud storage and computing services across major platforms like AWS (S3, Glue, Lambda), GCP (Cloud Storage, Dataflow), or Azure (Blob Storage, Data Factory).

- Communication &

- Collaboration:

Excellent verbal and written communication skills to collaborate effectively with data analysts, business intelligence teams, software developers, and product stakeholders.

📌 Data Engineer / Junior Data Engineer (Entry / Junior Level) (Victoria)
🏢 APSI-NE (Association des Professionnels de la Sécurité de l'Information du Niger)
📍 Victoria