Responsible to support data science projects and solutions by leveraging data and software engineering experience to solve a variety of use cases across the client and for its customers.
Expected to be highly skilled in batch and real-time data ingestion and data transformation with the ability to design, build and scale data pipelines for efficient data integration and processing.FUNCTIONAL KNOWLEDGE:

Relational and non-relational database foundational knowledge
Data engineering knowledge base
Data integration concepts and terms
Cloud computing and platform management ( AWS Reporting , etc.)



EXPERIENCE:

3-5 years relevant experience, of which at least 2 years must have been in a data engineering environment.
Experience in the banking industry will be an advantage.



JOB RESPONSIBILITIES:

Build data integration pipelines used to move data between databases, file systems & other locations while tracking data quality according to measures set by data owner.
Provide data support for specialized data centric services we provide to grow our data maturity:
This includes but are not limited to: Data modelling, database design, data quality assessments, data format conversion, data transformation, data tool selection.
Enable big data engineering by creating and managing code and tools created to efficiently obtain and move vast amounts of data between file systems and/or databases while ensuring data integrity.
Enable live/real-time data engineering by creating and managing code and tools built to efficiently obtain and move real-time data while ensuring data integrity.
Engage with stakeholders to support the design and delivery of data science projects and solutions.
Use data engineering techniques to solve business problems.
Responsible for working with a team of data engineers to develop and maintain our cloud-based development and production platforms.
Support ETL/ELT processes and data ingestion for exploratory data analysis and solution development.
Lead and develop a team of junior data engineers.
Contribute to our agile way of work and our innovation culture.
Up to date knowledge of data platforms and related technologies.
Translate business requirements into system requirements.
Consistent documentation of all implemented data integration pipelines and processes.
Support tools, data integration applications and infrastructure lifecycles via standard service management principles and processes.