Data Engineer (Enterprise Data & Intelligence)

Data Engineer (Enterprise Data & Intelligence)

Fourth

Sofia, Bulgaria

Position Overview

As a Data Engineer on the Enterprise Data & Intelligence team, you will design, build, and maintain the data pipelines and models that underpin Fourth’s single source of truth. You will gather and translate business requirements into reliable, well-tested data solutions on our Azure-based cloud platform. You will collaborate closely with analytics engineers and business stakeholders to scope, prioritise, and deliver data products that serve FP&A, Commercial, Product, and AI teams. As Fourth moves towards an AI-native operating model, you will play a key role in building and maintaining the data infrastructure that enables AI-powered analytics and decision-making across the business.

Responsibilities

Data Pipeline Development & Optimisation

• Build, maintain, and optimise scalable ETL/ELT pipelines (batch and near-real-time) on Azure Data Cloud Platform (e.g., Data Lake, Microsoft Fabric, Azure Data Factory).
• Develop and refine data models to support BI reporting, analytics, and ML/AI use cases.
• Write efficient, well-documented T-SQL and PySpark code following team coding standards.

Data Quality, Testing & Governance

• Implement automated testing, data validation, and monitoring (SLAs, alerts) to ensure pipeline reliability.
• Contribute to data governance practices, including lineage tracking, metadata management, and quality controls.
• Support CI/CD pipelines for data assets, ensuring version control and reproducibility.

Requirements Scoping & Cross-Functional Collaboration

• Partner with Analytics Engineers to scope, refine, and prioritise data requirements from business stakeholders.
• Work with Analysts, BI Developers, Data Scientists, and business teams to translate requirements into production-ready data solutions.
• Provide input on data readiness for machine learning and analytics projects.

Platform Contribution & Continuous Improvement

• Contribute to the evolution of the data platform, including tooling, standards, and documentation.
• Stay current with emerging data engineering patterns and technologies; propose improvements to team processes.
• Use AI as an integral part of the development workflow: code generation, data profiling, testing, documentation, and task management.
• Contribute to building a data platform that is AI-ready: well-documented, semantically clear, and structured for reliable consumption by AI tools and agents.
• Support performance tuning and cost optimisation across the data platform.

Key Skills and Competencies

• 3+ years in data engineering or a closely related role.
• Bachelor’s degree in Computer Science, Data Engineering, or a related field.
• Technical Skills:
  • Strong T-SQL skills and working proficiency in PySpark or Python for data processing.
  • Hands-on experience with MS Azure Storage Explorer and SSMS.
  • Hands-on experience with cloud-based data engineering services and orchestration tools (e.g., Azure Data Factory, Microsoft Fabric).
  • Practical experience building ETL/ELT pipelines and dimensional or analytical data models.
  • Familiarity with CI/CD practices in data engineering, including version control (Git) and automated testing.
  • Active use of AI productivity tools (e.g., ChatGPT, Claude, Copilot, Cursor) as an integrated part of development, testing, documentation, and day-to-day engineering workflows.
• Soft Skills:
  • Ability to work collaboratively with both technical and non-technical stakeholders.
  • Good documentation habits and a willingness to communicate technical concepts clearly.
  • Proactive and timely communication on progress, blockers, and dependencies with stakeholders and team members.
  • Strong analytical and problem-solving mindset.
  • Proactive, detail-oriented, and comfortable working in an agile, fast-paced environment.
  • Proficiency in English, both spoken and written.

Preferred Qualifications:

• Experience with real-time or streaming data architectures.
• Experience with PowerShell, Apache Kafka, and/or KQL.
• Exposure to AI/ML workflows (feature engineering, data preparation for model training).
• Familiarity with Power BI or other BI/visualisation tools.
• Understanding of data security, privacy, and compliance considerations.