Summary

The Data Architect is responsible for designing, implementing, and maintaining an organization's data architecture and strategy, ensuring that data is collected, stored, and processed efficiently and securely to support business intelligence, data analytics, and machine learning operations (MLOps) practices.

Key Responsibilities

1. Designing 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.
2. Implementing Data Engineering Pipelines: Design and develop data pipelines for data extraction, transformation, and loading (ETL) processes, ensuring data quality and consistency.
3. Enabling Data Intelligence and Analytics: Build and maintain data warehouses, data marts, and data lakes to support business intelligence and data analytics initiatives.
4. Supporting 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.
5. Ensuring Data Security and Compliance: Implement security measures, policies, and procedures to safeguard data privacy and comply with relevant regulations.
6. Data Governance and Management: Establish and enforce data governance policies and standards to ensure data quality, integrity, and accessibility.
7. Collaborating 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.
8. Staying 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.
9. Optimizing Data Performance: Monitor and analyze data processing performance, identify bottlenecks, and implement optimizations to enhance efficiency and scalability.
10. Documentation and Knowledge Sharing: Create and maintain comprehensive documentation of data architecture, models, and processing workflows.

Technical Requirements

1. Extensive experience in data architecture design and implementation.
2. Strong knowledge of data engineering principles and practices.
3. Expertise in data warehousing, data modelling, and data integration.
4. Experience in MLOps and machine learning pipelines.
5. Proficiency in SQL and data manipulation languages.
6. Experience with big data platforms (including Apache Arrow, Apache Spark, Apache Iceberg, and Clickhouse) and cloud-based infrastructure on AWS.

Education & Qualifications

1. Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent experience.
2. Preferred certifications (optional):
3. AWS Cloud Data Engineer
4. AWS Machine Learning Ops Engineer

Leadership & Collaboration

1. Passion for building scalable, reliable, and secure systems in a fast-paced environment.
2. Ability to translate complex technical concepts into clear, actionable insights for technical teams.
3. Strong interpersonal skills with the ability to work effectively across cross-functional teams.
4. Excellent problem-solving and analytical skills.

Skills

• AWS
• Communications Skills
• Development
• Leadership
• Problem Solving
• Software Architecture
• Software Engineering
• SQL
• Team Collaboration