Data Engineer – Enterprise Data & Intelligence

Fourth

. Design, build, and maintain data pipelines and models underpinning Fourth’s single source of truth .

Posted 9/21/2026full-timeSofia • BulgariaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and maintaining data pipelines and models, with strong proficiency in T-SQL and PySpark for data processing. Familiar with Azure Data Cloud Platform tools and CI/CD practices, ensuring high-quality data solutions and effective collaboration with stakeholders.

Highest-signal resume keywords

Data Pipeline DevelopmentT-SQL ProficiencyPySpark ProgrammingAzure Data FactoryETL/ELT Pipeline Optimization

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringT-SQLPySparkETL/ELT PipelinesData ModelingAutomated TestingData ValidationData GovernanceCI/CD PracticesAI Productivity Tools

Soft Skills

Analytical MindsetProblem-SolvingProactive CommunicationCollaborationDocumentation Skills

Tools & Technologies

Azure Data Cloud PlatformMS Azure Storage ExplorerSSMSMicrosoft FabricPower BIApache KafkaPowerShellGitChatGPTCursor

Industry Keywords

Data GovernanceData SecurityData PrivacyComplianceMachine LearningAnalyticsBusiness IntelligenceStreaming Data ArchitecturesMetadata ManagementData Quality Controls

Tech Stack

Tools & technologies

ApacheAzureCloudETLKafkaPySparkPythonSQL

About the role

Key responsibilities & impact

Design, build, and maintain data pipelines and models underpinning Fourth’s single source of truth
Gather and translate business requirements into reliable, well-tested data solutions on Azure
Build, maintain, and optimise scalable batch and near-real-time ETL/ELT pipelines using Azure Data Cloud Platform tools
Develop and refine data models for BI reporting, analytics, and ML/AI use cases
Write efficient, well-documented T-SQL and PySpark code
Implement automated testing, data validation, monitoring, SLAs, and alerts
Contribute to data governance, lineage tracking, metadata management, and quality controls
Support CI/CD pipelines for data assets, version control, and reproducibility
Partner with Analytics Engineers and business stakeholders to scope, refine, prioritise, and deliver data products
Collaborate with Analysts, BI Developers, Data Scientists, and business teams on production-ready data solutions
Provide input on data readiness for machine learning and analytics projects
Contribute to the evolution of data platform tooling, standards, and documentation
Stay current with data engineering patterns and technologies and propose process improvements
Use AI for code generation, data profiling, testing, documentation, and task management
Build an AI-ready data platform with clear documentation, semantics, and structure
Support performance tuning and cost optimisation across the data platform

Requirements

What you’ll need

3+ years in data engineering or a closely related role
Bachelor’s degree in Computer Science, Data Engineering, or a related field
Strong T-SQL skills
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, such as Azure Data Factory and Microsoft Fabric
Practical experience building ETL/ELT pipelines and dimensional or analytical data models
Familiarity with CI/CD practices in data engineering, including Git and automated testing
Active use of AI productivity tools such as ChatGPT, Claude, Copilot, or Cursor in development, testing, documentation, and engineering workflows
Ability to work collaboratively with technical and non-technical stakeholders
Good documentation habits and ability to communicate technical concepts clearly
Proactive and timely communication regarding progress, blockers, and dependencies
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
Experience with real-time or streaming data architectures preferred
Experience with PowerShell, Apache Kafka, and/or KQL preferred
Exposure to AI/ML workflows preferred
Familiarity with Power BI or other BI/visualisation tools preferred
Understanding of data security, privacy, and compliance considerations preferred

Benefits

Comp & perks

25+ days off
Birthday day off
4 charity days off per year
Flexible start and end of the working day
Hybrid working mode, including a combination of remote and in-office work
Supplemental health insurance
New parents bonus scheme
Team-centric atmosphere
Healthy lifestyle and work-life balance support