Chief Data Architect

As a pivotal member of our data engineering team, you will play a crucial role in designing, implementing, and optimizing data pipelines ensuring seamless integration with cloud platforms.

Design, develop, and maintain scalable and efficient data pipelines using Python and cloud-based technologies.

Implement Extract, Transform, Load (ETL) processes to seamlessly move data from diverse sources into our cloud-based data warehouse.

Utilize cloud platforms (e.g., Google Cloud, AWS, Azure) to deploy, manage, and optimize data engineering solutions.

Leverage cloud-native services for storage, processing, and analysis of large datasets.

Collaborate with data scientists, analysts, and other stakeholders to design effective data models that align with business requirements.

Ensure the scalability, reliability, and performance of the overall data infrastructure on cloud platforms.

Continuously optimize data processes for improved performance, scalability, and cost-effectiveness in a cloud environment.

Monitor and troubleshoot issues, ensuring timely resolution and minimal impact on data availability.

Implement data quality checks and validation processes to ensure the accuracy and completeness of data in the cloud-based data warehouse.

Work closely with data scientists, analysts, and other teams to understand data requirements and provide technical support.

Collaborate with other engineering teams to seamlessly integrate data engineering solutions into larger cloud-based systems.

Create and maintain comprehensive documentation for data engineering processes, cloud architecture, and pipelines.

• Data Pipeline Development: Design, develop, and maintain scalable and efficient data pipelines using Python and cloud-based technologies.
• Cloud Integration: Utilize cloud platforms (e.g., Google Cloud, AWS, Azure) to deploy, manage, and optimize data engineering solutions.
• Data Modelling and Architecture: Collaborate with data scientists, analysts, and other stakeholders to design effective data models that align with business requirements.
• Optimization and Performance: Continuously optimize data processes for improved performance, scalability, and cost-effectiveness in a cloud environment.
• Quality Assurance: Implement data quality checks and validation processes to ensure the accuracy and completeness of data in the cloud-based data warehouse.

• Strong problem-solving and critical-thinking abilities.
• Excellent communication skills, both written and verbal.
• Ability to work collaboratively in a cross-functional team environment.
• Attention to detail and commitment to delivering high-quality solutions.
• Proficiency in Python for data engineering tasks, scripting, and automation.
• Extensive experience with data engineering frameworks like distributed data processing.
• Understanding and hands-on experience with workflow management tools like Apache Airflow.
• In-depth knowledge and hands-on experience with at least one major cloud platform: AWS, Azure, or Google Cloud.
• Familiarity with cloud-native services for data processing, storage, and analytics.
• Proven expertise in designing and implementing Extract, Transform, Load (ETL) processes.
• SQL and Databases: Proficient in SQL with experience in working with relational databases (e.g., PostgreSQL, MySQL) and cloud-based database services.
• Data Modeling: Strong understanding of data modeling principles and experience in designing effective data models.
• Version Control: Familiarity with version control systems, such as Git, for tracking changes in code and configurations.
• Collaboration Tools: Experience using collaboration and project management tools for effective communication and project tracking.
• Containerization and Orchestration: Familiarity with containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes).
• Monitoring and Troubleshooting: Ability to implement monitoring solutions and troubleshoot issues in data pipelines.
• Data Quality Assurance: Experience in implementing data quality checks and validation processes.
• Agile Methodologies: Familiarity with agile development methodologies and practices.