The Data Engineer is responsible for designing, building, and maintaining scalable, reliable, and high-quality data pipelines and platforms that enable analytics, reporting, and data-driven decision-making. The role focuses on transforming raw data into trusted, accessible datasets while ensuring performance, security, and operational excellence across the data ecosystem.
Key Roles & Responsibilities
• Design, develop, and maintain scalable, reliable data pipelines for batch and real-time processing
• Ingest, transform, and curate data from multiple internal and external sources
• Build and optimize data models and datasets for analytics, reporting, and downstream consumption
• Ensure data quality, completeness, and accuracy through validation, monitoring, and reconciliation checks
• Implement and maintain data orchestration, scheduling, and automation workflows
• Optimize data processing performance and cloud resource utilization
• Collaborate with data architects to align implementations with enterprise data architecture standards
• Work closely with analysts, data scientists, and business teams to understand data requirements
• Support BI, analytics, and AI/ML use cases by delivering well-documented and trusted datasets
• Implement data security, access controls, and privacy requirements within data pipelines
• Troubleshoot and resolve data pipeline failures and performance issues
• Contribute to DevOps and CI/CD practices for data solutions
• Document data pipelines, transformations, and operational procedures
• Participate in code reviews and promote data engineering best practices
Qualifications & Experience:
• Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related field
• Master’s degree is an advantage but not mandatory
• 8+ years of experience in data engineering, analytics engineering, or backend engineering roles
• Strong hands-on experience building and maintaining ETL/ELT pipelines
• Proven experience working with Relational and NoSQL databases, Data warehouses and data lakes, Structured, semi-structured, and unstructured data, Experience with cloud data platforms (e.g., Azure, AWS, GCP)
