Senior Data Engineer

Job Description Data Engineering

Position: Senior Data Engineer Data Warehousing, Data Engineering & BI Reporting
Experience: 8+ Years
Key Technologies: Microsoft Fabric, Python, Apache Spark (PySpark), Power BI

Job Summary

We are looking for an experienced Senior Data Engineer with strong expertise in Data Warehousing, Data Engineering, Power BI reporting, and data visualisation. The candidate will be responsible for designing and maintaining scalable data platforms, developing efficient data pipelines, and delivering interactive dashboards and reports using Microsoft Fabric, Python, Spark, and Power BI to enable data-driven business decisions.

Key Responsibilities

Data Engineering & Data Warehousing

• Design, develop, and maintain scalable and high-performance data warehousing solutions.
• Build, implement, and optimize ETL/ELT pipelines for data extraction, transformation, integration, and loading.
• Develop and manage data solutions using Microsoft Fabric, including Lakehouse, Warehouse, and Data Factory capabilities, as applicable.
• Develop data processing and transformation workflows using Python and Apache Spark (PySpark).
• Design and implement data models, dimensional schemas, star/snowflake schemas, and efficient data structures.
• Integrate data from multiple sources while ensuring data accuracy, consistency, and reliability.
• Optimize data pipelines, Spark jobs, SQL queries, and warehouse performance.
• Implement data quality checks, monitoring, error handling, and recovery mechanisms.
• Ensure compliance with data governance, security, access control, and documentation standards.

Power BI Reporting & Data Visualisation

• Design, develop, and maintain interactive Power BI dashboards, reports, and visualisations.
• Translate business requirements into meaningful reports, KPIs, metrics, and actionable insights.
• Develop and optimize Power BI data models, relationships, DAX measures, and calculations.
• Create user-friendly dashboards with effective data visualisation techniques and drill-down capabilities.
• Integrate Power BI with Microsoft Fabric and other relevant data sources.
• Optimize report performance, data refresh schedules, and dashboard usability.
• Implement appropriate workspace management, Row-Level Security (RLS), and reporting access controls.
• Collaborate with business stakeholders to understand reporting needs and continuously enhance BI solutions.

Collaboration & Delivery

• Work closely with business stakeholders, BI developers, analysts, and technology teams to deliver end-to-end data solutions.
• Drive automation, technical improvements, and best practices across data engineering and BI reporting.
• Maintain technical documentation and support deployment, troubleshooting, and production activities.

Required Skills & Qualifications

• 8+ years of relevant experience in Data Engineering, Data Warehousing, Business Intelligence, or related roles.
• Strong hands-on experience in Microsoft Fabric.
• Proficiency in Python, Apache Spark, and PySpark.
• Strong SQL skills, including query optimization and performance tuning.
• Strong understanding of Data Warehousing architecture, ETL/ELT, and dimensional data modelling.
• Hands-on experience in Power BI report development, dashboard creation, and data visualisation.
• Experience with DAX, Power Query, data modelling, and Power BI performance optimization.
• Understanding of data governance, data quality, data security, and reporting controls.
• Strong analytical, problem-solving, and stakeholder management skills.

Preferred Skills

• Experience with Microsoft Azure data services and cloud-based data platforms.
• Familiarity with Git, CI/CD, and automated deployment practices.
• Experience with enterprise BI and analytics environments.
• Relevant Microsoft Fabric, Power BI, or Azure certifications will be an added advantage.

Key Competencies

• Strong technical ownership and end-to-end solution delivery.
• Ability to convert complex business requirements into scalable data solutions and clear visual insights.
• Effective communication and cross-functional collaboration.
• Continuous improvement and a data-driven mindset.