Lead Data Engineer

At Verto, we’re passionate about helping businesses in Emerging markets reach the world. What first started life as a FX solution for trading Nigerian Naira has now become a market-leading platform, changing the way thousands of businesses transfer money in and out of Emerging Markets.

We believe that where you do business shouldn’t determine how successful you are, or your ability to scale. Millions of companies a day have to juggle long settlement periods, high transaction fees and issues accessing liquidity in order to trade with African businesses. We’re on a mission to change this by creating equal access to easy payment and liquidity solutions that are already a given in developed markets.

We’re not alone in realising the opportunity and need to solve for emerging markets. We’re backed by world-class investors including Y-Combinator, Quona and MEVP, power payments for some of the most disruptive start-ups in the world and have a list of accolades from leading publications including being voted ‘Fintech Start Up of the Year’ at Fintech Awards London 2022.

Each year we process billions of dollars of payments and provide companies with solutions which help them to save money, automate processes and grow, but we’re only just getting started.

We’re seeking a driven and results-oriented Lead Data Engineer who is excited to help build out a best-in-class Data Platform.  In this role, you will be expected to achieve key milestones such as improving on our existing Data Warehouse, implementing a CI/CD framework, and enabling core technologies such as dbt and git.  You will play a pivotal role in enabling long-term scalability and efficiency when it comes to all things data, and leveraging your expertise in Data Engineering to drive measurable impact.

• Conceptualize, maintain and improve the data architecture

• Evaluating design and operational cost-benefit trade-offs within systems

• Design, build, and launch collections of data models that support multiple use cases across different products or domains

• Solve our most challenging data integration problems, optimising ELT pipelines, frameworks, sourcing from structured and unstructured data sources

• Implementing CI/CD frameworks

• Create and contribute to frameworks that improve the accuracy, efficiency and general data integrity

• Design and execute ‘best-in-class’ schema design

• Implementing other potential data tools

• Define and manage refresh schedules, load-balancing and SLA for all data sets in allocated areas of ownership

• Collaborate with engineers, product managers, and data analysts to understand data needs, identify and resolve issues and help set best practices for efficient data capture

• Determine and implement the data governance model and processes within ownership realm (GDPR, PPI, etc)

• Taking ownership of the data engineering process - from project scoping, design, communication, execution and conclusion

• Support and strengthen data infrastructure together with data team and engineering 

• Support organisation in understanding the importance of data and advocate for best-in-class infrastructure

• Mentoring, educating team members on best-in-class DE practices

• Priorising workload effectively

• Support quarterly and half-year planning from Data Engineering perspective

Note:  This is a fast-growing company, the ideal candidate will be comfortable leading other data engineers in the future.  However, this is currently a small data team, you may be asked to contribute to projects outside of the typical Data Engineering role.  This will most probably involve analytics engineering responsibilities such as maintenance and improvement of ‘core’ tables (transactions, companies, product/platform management).  

• University degree; ideally in data engineering, software engineering, computer science-engineering, numerate or similar

• +7 years of data engineering experience or equivalent

• Expert experience building data warehouses and ETL pipelines

• Expert experience of SQL, python, git, dbt (incl. query efficiency and optimization)

• Expert experience of Cloud Data Platforms (AWS, Snowflake and/or Databricks) → Qualification preferred, not mandatory

• Significant experience of Automation and Integrations tools (FiveTran, Airflow, Astronomer or similar)

• Significant experience with IoC tools (Terraform, Docker, Kubernetes or similar)

• Significant experience with CI/CD tools (Jenkins, GitHub Actions, CircleCI or similar)

• Experience with real time data pipelines (AWS Kinesis, Kafka, Spark)

• Experience with observability tools (Metaplane, MonteCarlo, Datadog or similar)

• Experience within FinTech/Finance/FX preferred