Job Title: Data Engineer

About Us:

Guavapay is a payment platform that drives payment optimisation and stability. Guavapay’s full-stack payment ecosystem includes a range of financial technology products and solutions. Our flagship product is MyGuava, the “all things payments app”. In addition to MyGuava, we offer a range of other payment services to businesses and individuals, including card processing, cross-border payments / remittance, merchant acquiring, card issuing and e-wallets. Whatever our customers’ needs are, our breakthrough platform can help them integrate, process and transact globally. We aim to take our place on the forefront of payment disruption, bringing innovative solutions for flawless and low cost payments on the market.

Our future plans extend beyond payments and remittance to other fintech areas. Our ambitions are global. Our drive for success is unmatched.

Role Overview:

We are looking for an experienced Data Engineer to create and maintain modern data infrastructure. The candidate should have deep knowledge of databases, ETL processes, and distributed computing. Experience with high-load systems and the ability to optimize query performance are essential requirements. We are seeking a proactive specialist ready to tackle complex challenges and actively participate in the development of our company.

Key Responsibilities:

• Creation and maintenance of ETL processes: Automating data collection from various sources (databases, APIs, files), transforming it, and loading it into a unified storage.
• Development and optimization of data pipelines: Building efficient data flows to ensure timely and complete loading of data into analytical systems.
• Building data marts: Developing a set of tables and views optimized for specific analytical tasks.
• SQL query optimization: Analyzing and improving query performance using indexes, materialized views, and other methods.
• Participation in data strategy development: Forming a long-term strategy for the development of data infrastructure.
• Exploring new technologies: Researching and implementing innovative solutions in data processing.

Requirements & Qualifications:

• Deep understanding of database principles: Experience with relational and NoSQL databases, proficiency in SQL, the ability to design optimal data schemas, and efficiently execute queries.
• Expertise in building data warehouses: Experience in creating both traditional data warehouses (Data Warehouse) and modern Data Lakes. Knowledge of design principles.
• Practical experience in ETL process development: Proficiency in ETL orchestration tools (Airflow, etc.), the ability to create reliable, scalable, and easily maintainable data pipelines.
• Experience with high-load systems: Experience with distributed data processing systems (Hadoop, Spark, S3), understanding parallel data processing principles, and optimizing the performance of complex computations.
• Knowledge of queue technologies: Experience with messaging systems (Kafka), the ability to design reliable and scalable data transmission systems.
• Programming: Excellent knowledge of at least one programming language: SQL, Python.
• Commitment to development: A constant desire to learn new technologies and tools in the field of data processing.