Data Engineering Expert
hace 4 semanas
Ebury, a leading fintech company, is seeking a seasoned Data Engineer to join its data engineering team. With a focus on delivering high-quality data solutions, this role offers the opportunity to work with cutting-edge technology and contribute to the growth of our platform.
About EburyEbury is a hyper-growth fintech firm that offers a range of products including FX risk management, trade finance, currency accounts, international payments, and API integration. Our company culture values innovation, collaboration, and customer satisfaction, making it an ideal place for professionals who thrive in dynamic environments.
Job DescriptionThe successful candidate will be responsible for designing, developing, and deploying ELT/ETL data pipelines from various data sources, including transactional databases, REST APIs, and file-based endpoints. They will also work closely with the data engineering team to maintain and optimize existing pipelines, ensuring data quality, accuracy, and compliance with business objectives.
A key aspect of this role is collaborating with cross-functional teams to reinforce best practices across the platform, encouraging a shared commitment to quality. The Data Engineer will also participate in data modelling reviews and discussions to validate model accuracy, completeness, and alignment with business objectives.
Required Skills and Qualifications- 3+ years of data/analytics engineering experience building, maintaining & optimising data pipelines & ETL processes on big data environments
- Proficiency in Python and SQL
- Knowledge of software engineering practices in data (SDLC, RFC...)
- Stay informed about the latest developments and industry standards in Data
- Fluency in English
We offer a comprehensive benefits package, including:
- Mentorship from outstanding performance team members along a 30/60/90 plan designed specifically for you
- Participation in data modelling reviews and discussions to ensure model accuracy and alignment with business objectives
- Design, development, deployment, and maintenance of ELT/ETL data pipelines from various data sources
- Servicing hands-on delivery of data models using solid software engineering practices
- Management of pipeline orchestration using Airflow, as well as execution using GCP hosted services
- Opportunities to reduce technical debt by addressing outdated, inefficient, or no longer aligned code
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