Data Engineer

We are seeking a talented and experienced Data Engineer to join our dynamic

team. As a Data Engineer, you will play a crucial role in designing, building, and

maintaining our data infrastructure to support our growing business needs.

Your role will include building a new data warehouse alongside the data team,

building data pipelines to provide data to both internal and external clients and

maintaining and ensuring data quality for the business.

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 4+ years experience as a Data Engineer or similar role, with a strong background in designing and building data pipelines, ETL processes, and data warehouses.
• Hands-on experience with cloud-based data platforms and services, particularly AWS (Amazon Web Services) including but not limited to S3, Redshift, Lambda and Glue
• Proficiency in programming languages such as Python, SQL, and experience with data processing frameworks/libraries such as Apache Spark, Pandas, or similar.
• Experienced in managing compute workloads and scaling with tools such as kubernetes
• Solid understanding of database concepts, data modelling, and relational databases (e.g., PostgreSQL, MySQL).
• Strong analytical, problem-solving, and communication skills, with the ability to work effectively in a fast-paced and collaborative environment.
• Experience working in the fintech or financial services industry is a plus.
• Excellent communication and collaboration skills, with the ability to interact effectively with technical and non-technical stakeholders
• Design and build data warehouse alongside the data team
• Design, develop, and maintain data pipelines and ETL processes to ingest, process, and transform large volumes of data from various sources into our data warehouse.
• Build data pipeline to third party reporting and automation tools
• Work with clients to provide clean and reliable data in a safe environment
• Build and optimize data models, schemas, and databases to support analytical and reporting requirements.
• Implement data quality checks, monitoring, and error handling mechanisms to ensure the integrity and reliability of our data.
• Collaborate with the data team and business departments to understand data requirements and deliver data solutions that meet business needs.
• Continuously optimize and improve data infrastructure, performance, and scalability to support growing data volumes and evolving business requirements.
• Stay up-to-date with emerging technologies and best practices in data engineering, cloud computing, and data management.