Position Purpose

The incumbent works as an innovative Data Engineer to join our Data and Analytics team and help develop scalable data solutions that support business intelligence and analytics initiatives. This role is best suited for individuals who enjoy solving complex data challenges and building end-to-end data pipelines and storage systems.

Key Responsibilities

• Integrate data from multiple sources, such as databases, APIs, or streaming platforms, to provide a unified view of the data.
• Design, develop, and maintain data pipelines for ETL processes to prepare data for storage and analysis.
• Manage storage solutions to enable efficient retrieval and backup of data.
• Collaborate with cross-functional teams, including data scientists, analysts, and business stakeholders.
• Implement data quality checks and validation processes to ensure the accuracy, completeness, and consistency of data.
• Optimize data processing and query performance by tuning database configurations, implementing indexing strategies, and leveraging distributed computing frameworks such as Apache Spark.
• Monitor data pipelines, databases, and data infrastructure for errors, performance issues, and system failures.
• Set up monitoring tools, alerts, and logging mechanisms to proactively identify and resolve issues to ensure the availability and reliability of data.
• Document data pipelines, data schemas, and system configurations, making it easier for others to understand and work with the data infrastructure.

• Bachelor’s or master’s degree in computer science, information technology, data engineering, or business intelligence.
• At least 7 years of experience working with databases and in a data engineering/business intelligence or backend data role.
• Strong proficiency in building and managing ETL/ELT pipelines.
• Strong proficiency in scripting languages (e.g., Python) and SQL for data manipulation.
• Expertise in working with relational databases (e.g., MS SQL Server, Oracle) and data warehousing concepts.
• Solid understanding of data governance, data security, and system logging.
• Strong knowledge of data processing frameworks like Apache Spark or Amazon Redshift, for efficiently handling large-scale data processing and transformation tasks.