Data Engineer

Paysend operates a global payment infrastructure with its own multi-jurisdictional license footprint and proprietary systems spanning processing, FX, orchestration, and settlement.

• Design, build, and maintain data pipelines and transformations on Databricks using Python, PySpark, and SQL.
• Deploy, operationalize, and monitor machine learning models in production; build infrastructure, automated MLOps pipelines, and serving architectures to deploy, monitor, and maintain ML solutions at scale.
• Build and maintain data marts as the primary source of truth for key business domains; apply dimensional modelling and medallion architecture to maintain data consistency and reusability.
• Implement data quality checks, monitoring, and alerting; optimize pipeline performance and cost-efficiency.
• Lead and contribute to engineering standards and process improvements across the data team.
• Act as the primary data partner for specific business areas, understanding their processes, goals, and pain points.
• Deliver end-to-end solutions, including scoping, discovery, design, delivery, user adoption, and iteration; gather and translate business requirements into technical solutions.
• Work with engineering peers and commercial stakeholders to define priorities and manage trade-offs; identify opportunities for data or machine learning to improve decisions, efficiency, or customer outcomes.
• Build and maintain datasets for reporting and dashboards in Preset; onboard and support users to promote data self-sufficiency.
• Promote data definitions, documentation, and governance.

• 5+ years of experience as a Data/ML Engineer, 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; 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 such as Preset, Tableau, Metabase, or Power BI, and data usability principles.
• Accountability for project outcomes and commercial impact; clear, structured communication in English.
• Nice to have: ML engineering experience deploying models to production, maintaining MLOps pipelines, and integrating models into data platforms; fintech, financial services, or payments experience, particularly with transaction, reconciliation, settlement, or regulatory reporting data; experience with sensitive or regulated data and privacy/governance such as GDPR; infrastructure as code (Terraform/Tofu), AWS, Azure, or GCP, and data engineering CI/CD; mentoring engineers or analysts; interest in AI tools for data engineering productivity and quality.

• Hybrid work in Belgrade.
• Opportunity to own meaningful outcomes, grow and develop, collaborate with colleagues from diverse backgrounds, and contribute to global money-movement products and services.