Senior Data Engineer (ML)

Location: Belgrade, Serbia; hybrid work

Company

hirify.global is a global fintech company building products and services for moving money internationally.

What you will do

• Design, build, maintain, monitor, and optimize data pipelines and transformations on Databricks using Python, PySpark, and SQL.
• Deploy and operationalize machine learning models in production, including the infrastructure, MLOps pipelines, and serving architectures required for scale.
• Build data marts and apply dimensional modelling and medallion architecture to provide consistent, reusable sources of truth for business domains.
• Implement data quality checks, monitoring, alerting, orchestration, testing, version control, and cost-efficiency improvements.
• Own end-to-end data and ML solutions from scoping and discovery through delivery, adoption, and iteration while partnering with Product, Finance, Operations, Compliance, Marketing, and engineering teams.
• Build datasets for reporting and dashboards in Preset, support user onboarding, and promote data definitions, documentation, governance, and self-service.

Requirements

• 5+ years of experience as a Data or ML Engineer, independently owning data or ML solutions from scoping through adoption.
• Proficiency in Python, SQL, and PySpark or Spark.
• Experience with modern data platforms such as Databricks, including Delta Lake, Unity Catalog, MLflow, and workflows.
• Stakeholder management skills across commercial and engineering discussions, including expectation management and cross-functional decision-making.
• Experience with data modelling and 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 knowledge of data usability principles.
• Demonstrated accountability for project outcomes and commercial impact.
• Clear, structured communication in English.

Nice to have

• Experience deploying machine learning models to production, maintaining MLOps pipelines, and integrating models into data platforms.
• Experience in fintech, financial services, or payments, especially transaction, reconciliation, settlement, or regulatory reporting data.
• Experience with sensitive or regulated data and data privacy and governance, including GDPR.
• Experience with infrastructure as code such as Terraform or Tofu, 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.

Culture & Benefits

• Opportunity to contribute to global money movement products used by millions of customers.
• Autonomy, ownership of meaningful outcomes, and the ability to make decisions and shape solutions.
• Learning, development, new challenges, and career growth across functions and geographies.
• Collaboration with colleagues from diverse backgrounds and disciplines.
• Culture focused on action, curiosity, experimentation, continuous improvement, and delivering customer outcomes.