Data Engineer (in Dubai)

About Hubpay

• Hubpay is the digital wallet for the remittance community providing access to mobile money. We are a licensed Money Services Business (MSB) which has launched a cross-border wallet and are currently one of only two MSB license holders in the UAE. Our first product to market is remittances and we are expanding internationally, launching our wallet in markets across Asia and expanding our product range to include loans, savings and insurance.

Responsibilities:

• Supported by the existing Data Science team, develop a thorough understanding of the existing technology stack that has been created for Data Science, the data it handles, the data processing it carries out and the business purposes it serves.
• Gradually take over responsibility for maintenance and upgrade of this technology stack, overseen by the Lead Data Scientist.
• Working under the guidance of the Lead Data Scientist, implement enhancements to the technology stack based on leading practices in DevOps/MLOps, to enable Data Science to adopt a CI/CD mode of operation and scale up as the business grows and develops.
• In support of this build, design and implement appropriate frameworks for automated unit testing and system integration testing (mainly for Python but including some R and Shiny).
• Carry out this build under an agile project management framework and standards for clean code and software testing that are overseen and supported by colleagues within the Data Science team.
• Deliver incremental enhancements to the platform, avoiding one-off big-bang delivery after a prolonged delay, which can be inefficient and costly.
• Maintain and adjust the enhanced technology stack, in accordance with the needs of the Data Science department and the wider business, dealing with bugs and issues that arise in accordance with priorities set by the Data Science Team.
• Train Data Science colleagues on new features of the platform.
• Support the Data Science team in use of the technology stack.Support Data Scientists in making sure that their code is fully production-ready (they are expected to produce code that is near-production-ready, but you may have to support them on the last mile).
• Act as a subject matter expert in engineering matters, advising other members of the Data Science department on these.
• Develop efficient solutions for computationally heavy operations required by data science (like rolling sums functions that become very time consuming in large data sets).
• Support the deployment of new data science use cases and new analytical approaches on the enhanced data science tech stack that you have created.

Requirements:

• A passion for all things related to DevOps/MLOps, data engineering and data science.Experience in Python & R (experience of Shiny is a bonus)
• Proficiency in proper software engineering disciplines, including good programming practice and robust testing disciplines.
• A passion for excellence in delivering up-to-date solutions in line with business need.
• Experience in deploying CI/CD pipelines.
• Advanced knowledge and experience of DevOps and/or MLOps.Experience of implementation of infrastructure-as-code.
• Knowledge of all stages of the machine learning and empirical modelling life cycle, including data extraction and wrangling, model development and testing, deployment, use, monitoring and re-development.
• Knowledge of other areas of data science, including descriptive statistics and data visualisation.
• Proven experience working with a) technical teams who have a different technical niche from you and b) non-technical teams, involving them in defining solutions, encouraging them to experiment and coaching them agile ways of working.
• Superior organisational, planning and problem-solving skills.
• Excellent communication skills (written, verbal, listening) and the ability to see where communication gaps exist and fix them.
• Excellent stakeholder management skills.

Beneficial but not essential:

• Good knowledge of financial services and trends in the consumer market.
• Experience of working across data science, engineering and product teams.
• Experience in financial services or another regulated industry.
• Experience of any of the following: marketing analytics, risk analytics, credit scoring, commercial optimisation, automation.

Qualifications:

• PhD, MSc or equivalent professional experience in data engineering, software engineering, AI or a related field.

Opportunity:

• A remuneration package comprising a competitive salary and other benefits.
• The chance to work in a fast-growing, innovative organisation that is making a positive difference in the world.
• Being part of the disruptive field and work directly with the founding team.
• Working for a start-up backed by global venture capital.