Data Engineer (Data Platform & Analytics) Full Time

The Data Engineer is responsible for designing and maintaining scalable ETL pipelines, optimising data warehouses, and ensuring data quality across platforms. The role includes building robust data solutions and interactive Power BI dashboards to enable analytics, reporting, and machine learning initiatives.

Key Responsibilities:

• Build, maintain, and optimise data pipelines and ETL processes for structured and unstructured data.
• Develop and maintain data warehouses, ensuring data quality, integrity, and accessibility.
• Design and maintain interactive dashboards and reports using Power BI to support business decision-making.
• Assist in the integration of data from multiple sources, including APIs, databases, and cloud platforms.
• Monitor data pipelines and troubleshoot issues to ensure smooth production operations.
• Support analytics and machine learning initiatives by providing clean, well-structured, reliable data.
• Collaborate with stakeholders to translate requirements into actionable datasets and reports.

Requirements:

• Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field.
• At least 4 years of experience as a Data Engineer, ETL Developer, or in a similar role.
• Proficiency in Python, SQL, and relational databases (e.g., PostgreSQL, MySQL, or SQL Server).
• Experience with Power BI for dashboards and reporting.
• Familiarity with cloud platforms (AWS, Azure, or GCP) and data warehousing solutions (e.g., Snowflake, Redshift, BigQuery).
• Solid understanding of ETL/ELT patterns, data modeling, and pipeline design.
• Experience with workflow orchestration tools like Apache Airflow, Prefect, or N8N.
• Familiarity with real-time streaming data and event-driven architectures.
• Basic understanding of machine learning pipelines, RAG (Retrieval-Augmented Generation), or LLM integrations.
• Experience with data visualisation tools (e.g., Tableau, Power BI, Looker).
• Strong analytical and problem-solving skills, with attention to data quality and accuracy.