Data Engineer
4 weeks ago
Sidetrade is a fast-growing international software company that is revolutionizing the Order-to-Cash process for global enterprises. Its AI-powered SaaS platform digitizes the financial customer journey, empowering CFOs to secure and accelerate cash flow generation. Recognized as a Leader in Gartner's Magic Quadrant for two consecutive years, Sidetrade fosters a culture of innovation, collaboration, and customer-centricity from its headquarters in Europe and North America.
We are seeking a passionate and knowledgeable Data Engineer with a multifaceted skill set. Immerse yourself in the exhilarating world of high-volume data & AI within our cutting-edge tech environment. Fuel your professional growth and innovation within our agile development ecosystem.
Key Responsibilities- Design, build, and maintain scalable and efficient data pipelines. Data is at the heart of everything we do. The pipelines you build will power our analytics, machine learning, and product features.
- Enhance our Data Lake and Data Warehouse to enable informed decision-making through data-driven insights.
- Develop highly efficient and well-tuned data platforms and products to ensure top performance, scalability, and reliability.
- Stay up to date with data engineering trends, recommending and implementing new technologies and tools to enhance data processing capabilities.
- Ensure unit tests have sufficient coverage and that code analysis is green.
- Support implementation of secure design principles according to policies and standards of Information Security.
- At least 3 years' experience in Data Engineering or Software Engineering roles.
- Excellent knowledge of SQL and Python.
- Experience with API development using Python frameworks such as FastAPI.
- Expertise in implementing programming best practices and principles.
- Proficient in relational and NoSQL databases.
- Experience developing and supporting robust, automated, and reliable data pipelines.
- Familiarity with data modelling techniques and data visualization tools.
- Attention to Detail: The success of downstream analytics, machine learning, and product features heavily relies on the integrity and quality of data.
- Sound knowledge of data architecture, scalability, and security/compliance.
- Build your internal network across all departments.
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