Data Platform Architect

Job Description

As a Data Platform Architect , you will play a pivotal role in the design, implementation, and optimization of scalable data architecture and systems. You will be responsible for creating data-driven solutions that enable effective data storage, integration, processing, and analytics. You will collaborate closely with data engineers, data scientists, and business stakeholders to build a robust data infrastructure that supports the organization’s data strategy. Qualifications: Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Engineering, or a related field. Proven experience (typically 7+ years) in designing and implementing complex data architectures and platforms. Expertise in cloud platforms (AWS, Azure, GCP) and their data services (e.g., Amazon Redshift, Azure Synapse, BigQuery). Strong knowledge of data integration, ETL/ELT tools, and data pipelines. Hands-on experience with big data technologies (e.g., Hadoop, Spark, Kafka). Proficiency in database technologies (SQL, NoSQL, columnar, graph, etc.). Familiarity with data modeling techniques and practices. Strong understanding of data security, governance, and privacy practices. Experience with data visualization and reporting tools (e.g., Power BI, Tableau). Excellent problem-solving, analytical, and troubleshooting skills. Strong communication and collaboration skills with technical and non-technical teams. Preferred Skills: Certifications in cloud platforms (e.g., AWS Certified Solutions Architect, Microsoft Certified: Azure Data Engineer Associate). Experience with containerized environments (e.g., Docker, Kubernetes). Familiarity with DevOps practices and CI/CD pipelines. Knowledge of machine learning and artificial intelligence applications in data platforms. Experience with Agile methodologies and project management tools. Roles and Responsibilities Key Responsibilities: Architect Data Solutions: Design and implement high-performance, scalable data platforms that support large-scale data ingestion, transformation, and analytics. Data Modeling: Develop and maintain data models and databases, ensuring they align with business requirements and are optimized for performance and usability. Cloud Infrastructure: Lead the adoption and implementation of cloud-based data platforms (e.g., AWS, Azure, Google Cloud) for data storage, processing, and analysis. Integration: Oversee the integration of disparate data sources and ensure seamless data flow across different systems (ETL/ELT processes). Performance Optimization: Continuously monitor, assess, and optimize the data platform architecture to ensure high performance, reliability, and scalability. Data Security & Governance: Implement best practices for data governance, privacy, and security to ensure compliance with internal policies and regulations. Collaboration: Work with cross-functional teams to understand data requirements, provide technical guidance, and deliver data solutions that meet business needs. Innovation & Research: Stay current with emerging data technologies and trends to drive innovation and improvements to the data platform. Documentation & Reporting: Maintain comprehensive documentation of data architecture, processes, and best practices for stakeholders and team members.