About the Role
The company is seeking a Senior Data Engineer to design, build, and maintain scalable and reliable data solutions within a modern cloud data environment.
The role focuses on Microsoft Fabric, Power BI, Python, and the development of large-scale data pipelines. The engineer will translate reporting and data requirements into robust data models, high-performance pipelines, and reusable data assets that help end users access reliable insights more efficiently.
The position requires an independent professional who can contribute not only to implementation, but also to architecture, data governance, quality, maintainability, and scalability.
Responsibilities
- Design, build, optimize, and maintain scalable data pipelines within a modern cloud data environment.
- Develop data integrations from relational databases, APIs, files, applications, and other data sources.
- Build and manage lakehouse, data warehouse, and medallion architectures.
- Focus on data reusability, performance, scalability, and quality.
- Translate reporting and analytics requirements into structured data models and reliable datasets.
- Monitor existing data flows, pipelines, and processes.
- Document and improve data pipelines and related processes.
- Independently manage data projects from analysis through implementation and delivery.
- Collaborate with business stakeholders, data analysts, architects, reporting specialists, and technical teams.
- Align requirements, proposed solutions, and deliverables with relevant stakeholders.
- Contribute to data engineering standards and best practices.
- Support knowledge sharing within the data team.
- Contribute to architecture, data governance, maintainability, and scalability decisions.
Requirements
- Demonstrable professional experience as a Data Engineer.
- Experience working with databases.
- Experience designing and implementing ETL processes.
- Experience developing data streams and ETL processes using Python, Java, Scala, or similar programming languages.
- Experience building scalable and reliable data pipelines.
- Ability to translate reporting and analytics requirements into data models and datasets.
- Ability to work independently.
- Strong understanding of data quality, maintainability, and scalability.
- Ability to collaborate with technical and non-technical stakeholders.
Technology Environment
- Microsoft Fabric.
- Power BI.
- Python.
- Java or Scala.
- ETL.
- Lakehouse architecture.
- Data warehouse architecture.
- Medallion architecture.
- Cloud-based data platforms.