We are looking for an experienced Technology Engineer with strong expertise in Microsoft SQL Server (MSSQL) and hands-on experience with Vector Databases.
The ideal candidate will be responsible for designing, implementing, and optimizing database solutions that support high-performance, large-scale applications, while also leveraging vector databases for AI/ML-driven workloads.
Requirements
Key Responsibilities
- Design, implement, and maintain MSSQL databases to ensure high availability, performance, and security.
- Develop and optimize complex queries, stored procedures, triggers, and functions.
- Implement backup, recovery, and disaster recovery strategies.
- Work with Vector Databases (such as Pinecone, Weaviate, Milvus, Chroma, or similar) to support AI/ML and semantic search use cases.
- Optimize data pipelines and indexing strategies for high-performance data retrieval.
- Collaborate with application development teams to design scalable and efficient database solutions.
- Monitor, troubleshoot, and resolve database performance issues.
- Ensure compliance with security, regulatory, and data governance policies.
- Stay updated on emerging trends in database technologies, particularly in vector search and AI-driven data management.
Required Skills & Experience
- 5 to 7 years of professional experience in database engineering/administration.
- Strong expertise in MSSQL Server (installation, configuration, optimization, replication, clustering, performance tuning).
- Hands-on experience with at least one Vector Database (e.g., Pinecone, Weaviate, Milvus, Chroma, Vespa, Qdrant).
- Proficiency in SQL programming and database design principles.
- Strong knowledge of ETL processes, data pipelines, and integration with applications.
- Experience with cloud environments (Azure, AWS, or GCP) and managed database services.
- Familiarity with AI/ML workloads, semantic search, and embeddings-based data retrieval is a plus.
- Excellent problem-solving, analytical, and communication skills.
Good to Have
- Knowledge of NoSQL databases (MongoDB, Cassandra, etc.).
- Exposure to Python or Java for integration with vector DBs.
- Experience with Kubernetes and containerized database deployments.