Key Responsibilities

📍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. 📍Implementing Data Engineering Pipelines: Design and develop data pipelines for data extraction, transformation, and loading (ETL) processes, ensuring data quality and consistency. 📍Enabling Data Intelligence and Analytics: Build and maintain data warehouses, data marts, and data lakes to support business intelligence and data analytics initiatives. 📍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. 📍Ensuring 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. 📍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. 📍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. 📍Optimizing 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.

Technical 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.

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