Data Platform Architect

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.

• 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.

• 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.

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.