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

Associate Data Analytics Engineer