Data Engineer

About the Role

We are seeking a talented and driven Data Engineer to join our team. The ideal candidate will be responsible for designing, developing, and maintaining robust data pipelines and architectures that support business analytics and data science initiatives. As a Data Engineer, you will work closely with data analysts, AI engineers, and other teams to ensure the efficient flow of data across the organization and enable data-driven decision-making. The role requires strong technical expertise in data systems, databases, and cloud infrastructure.

Responsibilities:

• Design, implement, and maintain scalable data pipelines to collect, process, and store large volumes of data from various sources.

• Develop and optimize ETL (Extract, Transform, Load) processes to integrate data from internal and external systems.

• Work with data analysts and other teams to ensure data is accessible and prepared for analysis and modelling.

• Manage and maintain databases, data lakes and warehouses, and other data storage systems.

• Collaborate with cross-functional teams to ensure data integrity, quality, and security.

• Implement data governance best practices to ensure data consistency and reliability across platforms.

• Optimize and troubleshoot data storage and query performance for large-scale datasets.

• Stay current with emerging data engineering technologies and methodologies to continuously improve processes and systems.

• Automate data collection and processing tasks where applicable to enhance operational efficiency.

Requirements:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, or a related field.

• 5 years’ experience in data engineering, architecture, or relevant software development.

• Proficiency in programming languages such as Python, Java, or Scala.

• Strong experience with SQL and NoSQL databases (e.g., MySQL, PostgreSQL, SQL Server, MongoDB).

• Expertise in data modelling, ETL processes, and data warehousing solutions.

• Experience with big data tools and frameworks (e.g., Hadoop, Spark, Hive, Kafka).

• Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes).

• Familiarity with data visualization tools (e.g., Power BI, Tableau) and business intelligence concepts.

• Strong problem-solving skills and the ability to work with large and complex datasets.

• Excellent communication skills and ability to work in a collaborative environment.

• Knowledge of AI/ML techniques and integration of AI/ML into production data pipelines.

• Knowledge of banking systems and financial data.

• Knowledge of data privacy, security, and compliance regulations.