# Senior Data Engineer at Paysend

Paysend is hiring a Senior Data Engineer. Level rates it Little AI ●○○○; you can [apply on Paysend](https://jobsbylevel.com/go/96a04550-02dc-4e40-88e7-696f2627e61d?utm_medium=ai-agent).

AI Level 1, AI centrality 12 out of 100. Beograd, Serbia.

## Details

- Company: [Paysend](https://jobsbylevel.com/companies/paysend)
- AI level: AI Level 1 (score 12 out of 100)
- Location: Beograd, Serbia
- Posted: October 24, 2026
- Apply: https://jobsbylevel.com/go/96a04550-02dc-4e40-88e7-696f2627e61d?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=senior-data-engineer-at-paysend-2cf81a

## Prepare for this job

The job page lists 3 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/senior-data-engineer-at-paysend-2cf81a

## 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 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.

The description is cut here. Read the full offer: https://jobsbylevel.com/jobs/senior-data-engineer-at-paysend-2cf81a

Source: https://jobsbylevel.com/jobs/senior-data-engineer-at-paysend-2cf81a

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