Sr Data Engineer

Competitive Compensation Stimulating Work Environment Wide Range of BenefitsPOSITION OVERVIEW

Kruger is seeking a Senior Data Engineer to join the Data Platform team and play a key role in designing, building, and delivering enterprise data products on Microsoft Fabric, to provide data solutions both on-prem and on cloud.
This role combines strong data engineering expertise with end user engagement. The successful candidate will be able to work directly with stakeholders from both business and technical fields to understand operational processes, discover and profile source data, translate business requirements into scalable technical solutions, and deliver trusted, reusable data products.
The ideal candidate is an experienced engineer who enjoys solving complex data problems and can adapt to fast development requirements, working with modern Lakehouse technologies, and collaborating across business and technical teams.

• Design, develop, and maintain scalable data products using Microsoft Fabric.
• Build and optimize data pipelines, Lake houses, Warehouses, Semantic Models, and related Fabric components.
• Build and deliver data solution for both on-prem and cloud environments.
• Develop ELT pipelines using SQL, Python, and Spark where appropriate.
• Design source-to-target mappings and transformation logic.
• Optimize data pipelines for scalability, reliability, and performance.
• Ensure solutions follow engineering best practices and enterprise standards.

• Partner with business stakeholders to understand operational processes, KPIs, and reporting requirements.
• Lead discovery sessions to identify business needs and data requirements.
• Profile new data sources to assess data quality, completeness, and business relevance.
• Translate business requirements into scalable technical solutions.
• Identify business rules, data relationships, and transformation requirements.
• Validate datasets with subject matter experts before production deployment.

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• Implement data validation and quality controls throughout data pipelines.
• Investigate and resolve data quality issues.
• Document source systems, mappings, transformations, and business rules.
• Support metadata management, data lineage, and governance initiatives.
• Ensure data products are trusted, reusable, discoverable, and well documented.

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• Participate in solution design and technical architecture discussions.
• Contribute reusable engineering patterns, templates, and standards.
• Perform code reviews and promote engineering best practices.
• Implement Git-based source control, CI/CD, and Data Ops practices.
• Monitor and continuously improve pipeline reliability and performance.
• Mentor junior engineers or interns and share technical knowledge across the team.

• Bachelor's or Master's degree in Computer Science, Software/Data Engineering, or a related technical field.

• 7+ years of experience in Data Engineering, Analytics Engineering, or a related field.
• Proven experience designing and delivering enterprise data solutions.
• Experience working directly with business stakeholders to gather requirements and validate solutions.
• Experience building scalable data pipelines.
• Experience with Git, CI/CD, and Data Ops practices.

• Strong programming in Python and SQL; hands-on experience with Spark / PySpark for distributed data processing.
• Proven experience building and operating a modern lakehouse or cloud data platform. Experience with Microsoft Fabric, Azure Data Factory, Synapse Analytics, Databricks, Snowflake, or similar modern data platforms.
• Strong understanding of Lakehouse architecture, data modeling, and ELT design.
• Experience building scalable data pipelines.
• Familiarity with Delta Lake or similar modern storage technologies.
• Experience with Git, CI/CD, and Data Ops practices.
• Understanding of data quality frameworks and automated testing.
• Demonstrated ability to own projects from discovery through production deployment.
• Excellent communication and stakeholder management…