Data Architect

Summary

Join Keyrock as a Data Architect and be responsible for designing, implementing, and maintaining the organization's data architecture and strategy. You will ensure efficient and secure data collection, storage, and processing to support business intelligence, data analytics, and MLOps. Key responsibilities include designing data architecture, implementing data engineering pipelines, enabling data intelligence and analytics, supporting MLOps practices, ensuring data security and compliance, and collaborating with cross-functional teams. The ideal candidate possesses extensive experience in data architecture, strong knowledge of data engineering principles, and expertise in data warehousing and modeling. A Bachelor's or Master's degree in a related field or equivalent experience is required. Keyrock offers a competitive salary package, flexible working hours, remote work opportunities, and a collaborative work environment.

Requirements

• Extensive experience in data architecture design and implementation
• Strong knowledge of data engineering principles and practices
• Expertise in data warehousing, data modelling, and data integration
• Experience in MLOps and machine learning pipelines
• Proficiency in SQL and data manipulation languages
• Experience with big data platforms (including Apache Arrow, Apache Spark, Apache Iceberg, and Clickhouse) and cloud-based infrastructure on AWS
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent experience

Responsibilities

• Design Data Architecture: Plan and implement a robust, scalable data architecture that integrates data from various sources and supports diverse analytical needs, while optimizing costs and meeting business requirements
• Implement Data Engineering Pipelines: Design and develop data pipelines for data extraction, transformation, and loading (ETL) processes, ensuring data quality and consistency
• Enable Data Intelligence and Analytics: Build and maintain data warehouses, data marts, and data lakes to support business intelligence and data analytics initiatives
• Support MLOps Practices: Collaborate with data scientists and machine learning engineers to design and implement data infrastructure and processes that support machine learning model development, deployment, and maintenance
• Ensure Data Security and Compliance: Implement security measures, policies, and procedures to safeguard data privacy and comply with relevant regulations
• Data Governance and Management: Establish and enforce data governance policies and standards to ensure data quality, integrity, and accessibility
• Collaborate with Cross-Functional Teams: Work closely with data engineers, data scientists, business analysts, and other stakeholders to understand data requirements and translate them into technical solutions
• Stay Abreast of Technological Advancements: Keep up-to-date with emerging technologies and trends in data architecture, data engineering, and MLOps to identify opportunities for improvement and innovation
• Optimize Data Performance: Monitor and analyze data processing performance, identify bottlenecks, and implement optimizations to enhance efficiency and scalability
• Documentation and Knowledge Sharing: Create and maintain comprehensive documentation of data architecture, models, and processing workflows

Preferred Qualifications

• AWS Cloud Data Engineer
• AWS Machine Learning Ops Engineer

Benefits

• A competitive salary package, with various benefits depending on method of engagement (Employee vs Contractor)
• Autonomy in your time management thanks to flexible working hours and the opportunity to work remotely
• The freedom to create your own entrepreneurial experience by being part of a team of people in search of excellence