Senior Data Engineer
Senior Data & ML Engineer at Paysend (Belgrade) owning data solutions end-to-end: building pipelines and data marts on Databricks with Python, PySpark and SQL, operationalizing ML models via MLOps, and acting as data partner to commercial stakeholders. Paysend is a global fintech moving money worldwide.
- Compensation
- Not disclosed
- Location
- Beograd, RS
- Work setup
- Hybrid
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 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 across commercial and engineering discussions
- 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
- ML Engineering experience: deploying models to production, maintaining MLOps pipelines, integrating models into data platforms
- Experience in fintech, financial services, or payments (transaction, reconciliation, settlement, regulatory reporting data)
- Experience with sensitive or regulated data and data privacy/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
Full description
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 DoingData 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.