We need an experienced Data Engineer to lead the transformation of legacy Microsoft SQL Server database to a scalable and high-performance NoSQL solution . The ideal candidate will play a critical role in redesigning the data architecture to improve performance, scalability, and flexibility for modern data workloads. Key Responsibilities: Assess and understand the existing MS SQL Server-based data schema, workloads, and performance bottlenecks. Evaluate and recommend appropriate NoSQL technologies (e.g., MongoDB, Cassandra, DynamoDB, Couchbase) based on application use cases. Design and implement scalable NoSQL data models optimized for high-throughput and low-latency operations. Migrate data and application logic from SQL to the chosen NoSQL solution with minimal downtime and data integrity. Develop robust data pipelines for ingestion, transformation, and real-time data processing. Work closely with application developers to refactor data access logic for NoSQL compatibility. Ensure best practices in data security, availability, consistency, and disaster recovery. Monitor and optimize NoSQL cluster performance, query tuning, and data indexing. Establish governance, documentation, and training materials to support the new architecture. Qualifications: Bachelors or Masters degree in Computer Science, Data Engineering, or related field. 5+ years of experience in data engineering, with a strong background in both SQL and NoSQL database systems. Proven track record of migrating from MS SQL Server to NoSQL databases in production environments. Deep understanding of NoSQL data modeling and trade-offs (e.g., consistency vs availability). Proficiency in one or more NoSQL platforms (e.g., MongoDB, Cassandra, DynamoDB, Redis, etc.). Strong experience with data migration tools, ETL/ELT processes, and real-time data streaming (Kafka, Spark, etc.). Excellent communication and documentation skills.