Data Architect / Senior Data Engineer, Keyrus

• The ideal candidate will have a proven track record in designing and building scalable data solutions, ensuring robust data pipelines, and managing complex ETL processes across cloud platforms
• 10+ years in Data Engineering and Architecture with a focus on data platforms, pipelines, and integration processes in enterprise settings
• Track record of successful project delivery in ETL, real-time data processing, and cloud data management (especially using AWS, Snowflake, and Talend)
• Strong consultative background with responsibility for end-to-end data solution delivery
• Data warehousing (e.g., Snowflake, Microsoft SQL Server) and ETL/ELT expertise
• Proficiency in Python for data processing and automation
• In-depth knowledge of AWS and Azure for data engineering and analytics
• Experience with data mesh and modern architectural frameworks
• Any of the following certifications: Snowcore Pro, Microsoft Certified: Azure Data Engineer Associate, Talend Data Integration v7 Certified Developer

Desirable

• We are seeking an experienced Data Architect / Senior Data Engineer to lead the design, development, and optimization of our data architecture and engineering practices
• Leveraging their expertise, the Data Architect / Senior Data Engineer will play a pivotal role in advancing our data-driven initiatives and enabling effective decision-making through high-quality data solutions
• Data Solution Architecture: Design and maintain comprehensive data architectures, including data warehouses, lakes, and meshes, ensuring alignment with organizational goals and data governance
• Data Pipeline Management: Build scalable ETL/ELT processes with tools like Talend and Snowflake; ensure real-time data processing using CDC and advanced data integration techniques
• Cloud Data Management: Use cloud platforms such as AWS and Azure to support data warehousing and analytics. Integrate and synchronize data from diverse sources for cross-functional accessibility
• Enhance operational efficiency by optimizing data models, storage, and processing speeds across all data solutions
• Engage with cross-functional teams to identify data requirements, resolve issues, and improve data accessibility
• Uphold high standards of data privacy, security, and compliance with regulatory requirements throughout the data lifecycle

• We usually respond within a week