Gpu Architecture Engineer

Roles and responsibilities

• Data Engineering: Implement and maintain data pipelines, ETL processes, and data integration patterns using AWS Glue, and other relevant tools.
• Data Warehousing and Data Marts: Design and build data warehouses and data marts to support analytical reporting and business intelligence.
• Database Administration: Manage and optimize database systems (e.g., PostgreSQL, MySQL, Redshift) to support data-driven applications.
• Data Quality: Implement data quality checks and validation processes to ensure data accuracy and consistency.
• Data Optimisation: Optimise data storage, retrieval, and processing to improve query performance and reduce costs.
• Stakeholder Collaboration: Work closely with cross-functional teams to understand data needs and deliver effective solutions.
• Innovation: Stay updated on industry trends and emerging technologies to drive continuous improvement in data engineering practices.
  
  

The Successful Applicant

• Minimum of 5 years of experience in data engineering, with a strong focus on AWS or open-source technologies.
• Bachelor's degree in computer science, data engineering, or a related field.
• Proven expertise in designing and implementing scalable data infrastructure solutions.
• Strong proficiency in SQL and database management systems (e.g., PostgreSQL, MySQL, Redshift).
• Experience with data warehousing and data mart concepts.
• Knowledge of data integration patterns and ETL processes.
• Familiarity with data quality best practices.

Desired candidate profile

• Database Design and Management: Proficiency in relational and non-relational databases (e.g., SQL, NoSQL).

• Data Modeling: Experience with conceptual, logical, and physical data models, including ER diagrams.

• ETL Processes: Knowledge of Extract, Transform, Load (ETL) processes and tools (e.g., Talend, Apache Nifi).

• Big Data Technologies: Familiarity with big data frameworks and tools (e.g., Hadoop, Spark, Kafka).

• Cloud Platforms: Experience with cloud data services (e.g., AWS, Azure, Google Cloud).

• Data Governance: Understanding data quality, metadata management, and compliance standards.

• Programming Skills: Proficiency in programming languages (e.g., Python, Java, SQL).

• Data Warehousing: Knowledge of data warehousing concepts and technologies (e.g., Snowflake, Redshift).

• Business Intelligence Tools: Familiarity with BI tools (e.g., Tableau, Power BI) for data visualization.

• Communication Skills: Ability to communicate complex data concepts to non-technical stakeholders.

• Problem-Solving Skills: Strong analytical skills to address data-related challenges.

• Collaboration: Experience working with cross-functional teams, including data scientists and engineers.