![]() Excellent analytics and problem-solving skills, with the ability to effectively communicate technical concepts to both technical and non-technical stakeholders.Solid understanding of various data storage technologies, including RDBMS, NoSQL, Graph, Vector databases, and object storage systems.Experience with Data & AI Python framework or libraries, such as Pandas, Numpy, TensorFlow, Pytorch, or scikit-learn etc.Strong programming skills in SQL, Python, PySpark.Proficiency in DataOps, MLOps, FinOps for data platform, Data Observability practices.In-depth understanding of Data Warehousing, Data Lake, Lakehouse, and data streaming concepts and their practical implementation. ![]() ![]() String expertise in Azure and AWS native data services, including but not limited to Amazon S3, Amazon Redshift, AWS Glue, Amazon Athena, AWS Lake Formation, Azure Data Lake Store, Amazon Kinesis, Azure Synapse Analytics, Azure Data Factory, Azure Databricks, Azure Event Hub.10 to 15 years of experience as a Data & AI Architect, with a proven track record of designing and implementing large-scale data solutions.Collaborate with cross-functional teams to ensure seamless integration of Data & AI solutions within the existing technology landscape.Stay updated with the latest advancements in Data & AI technologies and recommend relevant tools and frameworks to enhance the organization's capabilities.Provide technical leadership and mentorship to junior team members, promoting knowledge sharing and growth.Implement best practices for data engineering, data integration, data transformation, and data visualization.Collaborate with DataOps and MLOps teams to establish data pipelines, automation workflow, and monitoring processes.Define and implement data governance and data management strategies to ensure data quality, privacy, and compliance.Evaluate and recommend appropriate data storage technologies based on project requirements, such as relational databases, NoSQL databases, Graph databases, Vector databases, and object storage systems.Develop and implement data architectures, including Data Warehouse, Data Lake, Lakehouse, and data streaming solutions.Collaborate with stakeholders, including business users, data engineers, data scientists, and DevOps team, to define and refine architecture strategies and requirements.Design and architect end-to-end Data & AI solutions, considering business requirements, reliability, scalability, performance, and security. ![]()
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