Data Engineer

Purpose of Role

Builds, maintains, and optimizes the infrastructure required for data generation, storage, processing, and access. Designs reliable data architectures, pipelines, databases, and data warehouses to ensure clean, secure, scalable, and accessible data for analytics, reporting, digital transformation, and business decision-making.

Job Description :

• Design, build, and manage reliable data pipelines to support data ingestion, transformation, integration, and delivery across business systems and analytics platforms.
• Develop, maintain, and enhance databases, data lakes, and data warehouses to ensure data is efficiently stored, organized, secured, and accessible for reporting and analytical use.
• Implement ETL and ELT processes to extract, transform, validate, and load data from multiple internal and external sources into enterprise data platforms.
• Ensure data quality, reliability, consistency, and availability by applying data validation, cleansing, monitoring, reconciliation, and error-handling controls.
• Optimize data systems, queries, storage structures, and processing workflows for performance, scalability, cost efficiency, and operational resilience.
• Develop and maintain data models, schemas, metadata, and documentation to support clear data lineage, reusability, governance, and knowledge transfer.
• Collaborate with data analysts, application teams, infrastructure teams, cybersecurity, and business users to understand data requirements and deliver fit-for-purpose data solutions.
• Monitor, troubleshoot, and support data platform operations, including pipeline failures, data access issues, system performance, and production incidents.

Job Requirements :

• Bachelor’s Degree in Computer Science, Information Technology, Data Engineering, Data Science, Software Engineering, or a related discipline.
• Minimum 3–5 years of experience in data engineering, database development, data warehousing, ETL development, big data platforms, cloud data services, or related roles.
• Professional certifications in cloud data platforms, database technologies, or big data tools such as Microsoft Azure Data Engineer, AWS Data Analytics, Google Cloud Data Engineer, Databricks, Snowflake, or equivalent are highly desirable.
• Proven experience in designing, building, and managing data pipelines, ETL processes, databases, data lakes, and data warehouses for enterprise reporting and analytics requirements.
• Strong knowledge of programming languages such as Python, Java, or Scala; SQL and NoSQL databases; big data tools such as Hadoop or Spark; ETL frameworks; and cloud platforms such as AWS, Azure, or GCP.
• Good understanding of data governance, data quality, data security, metadata management, and performance optimization practices across structured and unstructured data environments.
• Technical and infrastructure-focused approach, with strong problem-solving skills and the ability to collaborate with analytics, application, infrastructure, and business teams to deliver reliable data solutions.