Data Engineer (MOE)

Company Overview Established in 2010, Vinova is an award-winning development company specializing in mobile, web, and enterprise applications. We serve global clients across IoT, blockchain, fintech, banking, networking, and ecommerce, delivering quality products through passionate, collaborative teams. Job Summary Design, build, and operate scalable data architectures and pipelines to support diverse data use cases. Collaborate cross-functionally to develop robust data models and apply modern engineering practices that enhance data system reliability, security, and performance. Responsibilities • Design, build, and operate scalable data architectures and pipelines for ingesting, transforming, and serving data across diverse source systems and use cases • Develop robust data models and reusable data capabilities for applications, analysts, data scientists, and other data consumers • Apply proven architectural and engineering practices to improve the reliability, security, observability, performance, and maintainability of data systems • Evaluate technologies and architectural approaches, make sound technical trade-offs, and contribute to evolving the Data Programme's architecture and engineering standards • Champion modern software engineering practices including automated testing, code review, CI/CD, and infrastructure-as-code, and help the team meet these standards through review and coaching • Collaborate cross-functionally with engineers, Product Managers, Data Scientists, analysts, and users, providing technical leadership through design reviews, mentoring, and knowledge sharing • Demonstrate strong software engineering fundamentals by developing and maintaining high-quality codebases • Utilize proficiency in Python and SQL to implement data solutions and perform data manipulation tasks • Apply strong experience in enterprise data architecture and engineering to design scalable and efficient data systems • Design data pipelines and data models that meet business and technical requirements • Use cloud platforms, preferably AWS, to deploy and manage data infrastructure • Work with modern data warehouse or data platforms such as Redshift, Snowflake, Databricks, BigQuery, or equivalents • Implement data orchestration, transformation, and modeling using modern engineering tools and approaches • Employ production engineering practices including testing, CI/CD, monitoring, troubleshooting, and ensuring data quality Preferred competencies and qualifications • Hands-on experience with workflow orchestration tools such as Apache Airflow or equivalent • Experience with transformation and analytics engineering frameworks such as dbt or equivalent