Job Purpose

Responsible for supporting the development and maintenance of data pipelines, semantic models, and business intelligence solutions to deliver reliable and accessible data for reporting and decision-making. Assists with data transformation, quality validation, reporting development, and stakeholder support while building foundational expertise in data engineering and analytics.

Key Result Responsibilities

• Assist in building and maintaining ELT/ETL pipelines that source, clean, and transform data for analytics consumption
• Write well-structured, documented SQL and dbt models to support data transformation and semantic layer development
• Support the development and maintenance of Power BI reports and dashboards under the guidance of senior engineers
• Help maintain and document data models in Snowflake or Azure Synapse, including basic schema design and table structures
• Perform data validation and quality checks to ensure accuracy and consistency of reporting outputs

Key Result Responsibilities-Continued

• Monitor scheduled pipeline runs and report data quality or performance issues with sufficient context for resolution
• Maintain documentation for pipelines, data flows, semantic model definitions, and report logic
• Participate in code reviews and team discussions to build awareness of engineering and analytics standards
• Collaborate with analysts and business stakeholders to understand reporting requirements and translate them into data tasks

Qualifications (Academic, training, languages)

• Bachelor's degree in Computer Science, Information Technology, Business Analytics, Statistics, or a related field.
• Fluent in English langage.
• Proficiency in SQL; exposure to Python is an advantage
• Basic familiarity with Power BI — creating reports, simple DAX measures, and working with data models
• Understanding of core data concepts: joins, aggregations, data types, and basic normalization
• Awareness of ELT/ETL processes and how data flows from source systems to reporting layers
• Basic familiarity with data warehousing concepts (facts, dimensions, star schema)
• Understanding of version control using Git
• Exposure to cloud data platforms such as Azure, AWS, or GCP

Work Experience

• With 1–2 years of experience in data engineering, analytics, BI development, or a related technical role (including internships or academic projects).
• ITIL Certification is an advantage but not mandatory