About the role
We’re looking for a highly autonomous Senior Data & ML Engineer who wants to be the true owner of how data works for the teams they support, delivering solutions completely end-to-end, from initial scoping and design through to delivery, implementation, and full business adoption.
Operating with full autonomy, you will work directly with both commercial stakeholders (Product, Finance, Operations, Compliance, and Marketing) and engineering teams. You will translate complex needs into well-defined and well-modelled data products, using scalable processes and pipelines, ensuring those solutions are trusted, documented, and actively used to drive business value.
Beyond foundational data architecture, you will support and lead machine learning engineering efforts. You will lead machine learning engineering efforts by transitioning predictive models into production. This involves building the infrastructure, automated MLOps pipelines, and serving architectures required to deploy, monitor, and maintain machine learning solutions at scale.
What You’ll Be Doing
Data Engineering and Platform
- Design, build, and maintain data pipelines and transformations on Databricks using Python, PySpark, and SQL.
- Deploy, operationalize, and monitor machine learning models (MLOps) in production environments.
- Build and maintain data marts as the primary source of truth for key business domains.
- Apply data modelling practices (e.g., dimensional modelling, medallion architecture) to maintain data consistency and reusability.
- Implement data quality checks, monitoring, and alerting.
- Optimise pipeline performance and cost-efficiency.
- Lead and actively contribute to engineering standards and process improvements across the data team.
Ownership and Stakeholder Management
- Act as the primary data partner for specific business areas, understanding their processes, goals, and pain points.
- Deliver end-to-end solutions, managing scoping, discovery, design, delivery, user adoption, and iteration.
- Gather and translate business requirements into technical solutions.
- Communicate with engineering peers and commercial stakeholders to define priorities and manage trade-offs.
- Identify opportunities where data or machine learning can improve decisions, efficiency, or customer outcomes.
Enablement and Self-Service
- Build and maintain datasets that power reporting and dashboards in Preset.
- Onboard and support users on Preset to promote data self-sufficiency.
- Promote data practices, including data definitions, documentation, and governance.
What You’ll Need to Be Successful
- 5+ years of experience as a Data/ML Engineer, with a track record of independently owning data/ML solutions from scoping to adoption.
- Proficiency in Python, SQL, and PySpark/Spark.
- Experience with modern data platforms, such as Databricks (Delta Lake, Unity Catalog, MLflow, workflows).
- Stakeholder management skills, with the ability to navigate commercial and engineering discussions, manage expectations, and drive cross-functional decisions.
- Data modelling skills and experience designing data marts for analytics and reporting.
- Experience building production pipelines with orchestration, testing, monitoring, and version control.
- Experience with BI tools (e.g., Preset, Tableau, Metabase, Power BI) and data usability principles.
- Demonstrated accountability for project outcomes and commercial impact.
- Clear, structured communication in English.
Nice to Have
- Experience in ML Engineering, including deploying machine learning models to production, maintaining MLOps pipelines, and integrating models into data platforms.
- Experience in fintech, financial services, or payments, particularly regarding transaction, reconciliation, settlement, or regulatory reporting data.
- Experience with sensitive or regulated data and an understanding of data privacy and governance (e.g., GDPR).
- Experience with infrastructure as code (Terraform/Tofu), cloud platforms (AWS, Azure, or GCP), and data engineering CI/CD.
- Experience mentoring engineers or analysts.
- Interest in applying AI tools to improve data engineering productivity and quality.
Why Join Paysend?
- Make a Global Impact: Join a company that is transforming how people and businesses move money around the world. Your work will contribute to products and services that help millions of customers stay connected globally.
- Own Meaningful Outcomes: We empower our people to take ownership, make decisions, and drive real impact. You'll have the opportunity to shape solutions, challenge the status quo, and see the results of your work firsthand.
- Grow with Us: As a fast-growing global fintech, we offer opportunities to learn, develop new skills, take on new challenges, and advance your career across functions and geographies.
- Collaborate with Exceptional People: Work alongside talented colleagues from diverse backgrounds and disciplines who are united by a shared ambition to build, innovate, and deliver great outcomes for our customers.
- Move Fast and Make Things Happen: We value action, curiosity, and continuous improvement. You'll be part of a culture that encourages experimentation, embraces change, and focuses on delivering meaningful results.
- Be Part of Our Journey: We're building the future of the global money movement. Join us at an exciting stage of growth where your ideas, expertise, and contributions can help shape what's next.
