Principal Database Engineer – SQL Server & AI Data Infrastructure

Job description

**This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Principal Database Engineer – SQL Server & AI Data Infrastructure based in United States.** The Principal Database Engineer will help modernize and scale enterprise data platforms that support critical business applications and next-generation AI capabilities. This is a deeply technical, hands-on role focused on SQL Server, T-SQL, database performance, scalability, and modernization. You will work with high-volume environments containing complex data relationships and business-critical workloads. The role combines traditional database engineering expertise with cloud data platforms, automation, event-driven architectures, and AI-enabled engineering practices. You will partner across application engineering, SRE, operations, architecture, and AI/ML teams while independently driving complex initiatives from design through production. This is an opportunity to remain highly technical while having Senior Staff-level ownership and influence over the evolution of modern data infrastructure. ### Accountabilities: - Design, develop, optimize, and modernize large-scale SQL Server databases and data platforms, with a strong focus on scalability, maintainability, reliability, and long-term technical health. - Analyze and refactor complex T-SQL, stored procedures, functions, and database logic while identifying opportunities to simplify and improve legacy systems. - Diagnose and resolve database performance challenges through execution-plan analysis, indexing strategies, query optimization, database tuning, and detailed analysis of database internals. - Drive schema modernization and migration initiatives, developing automation and implementation strategies that protect data integrity and minimize disruption to business-critical systems. - Engineer replication, synchronization, data integration, and data movement patterns across transactional and analytical environments, including SQL Server, PostgreSQL, Snowflake, and cloud data services. - Design and support high-volume OLTP and OLAP environments while applying strong knowledge of indexing, locking, transactions, isolation levels, concurrency, and query optimization. - Implement event-driven database patterns such as Change Data Capture, outbox patterns, and event publishing to support modern distributed architectures. - Apply AI coding assistants and emerging AI technologies to accelerate development, debugging, documentation, modernization, and database engineering workflows. - Contribute to AI-enabled data infrastructure, including embedding pipelines, vector databases, semantic search, retrieval systems, and data capabilities supporting AI applications and agentic workflows. - Independently lead complex database engineering initiatives from problem definition through design, implementation, deployment, and production adoption. - Serve as a technical authority for complex database and performance challenges, providing guidance through design reviews, code reviews, engineering standards, and technical decision-making. - Mentor other engineers and contribute to the development of scalable database engineering practices, standards, tooling, and automation. - Investigate high-impact production incidents using database metrics, logs, CPU and memory utilization, execution plans, and observability data, driving root-cause analysis and durable corrective actions. - Collaborate closely with application engineering, SRE, operations, architecture, data engineering, and AI/ML teams to ensure reliable delivery and continuous improvement of critical data platforms. ### Requirements - 15+ years of experience in database engineering, data platform engineering, software engineering, or a related technical discipline, with substantial experience operating at enterprise scale. - Deep hands-on expertise with Microsoft SQL Server and T-SQL, including complex stored procedures, execution-plan analysis, indexing, query optimization, database tuning, and performance troubleshooting. - Demonstrated experience modernizing, refactoring, or transforming legacy database environments rather than simply maintaining existing systems. - Strong experience supporting large-scale, high-volume, business-critical databases and applications where reliability, performance, and data integrity are essential. - Strong programming skills in Python, C#, or a comparable language, with the ability to develop automation, engineering tools, integrations, and data-processing solutions. - Experience with data pipelines, ETL/ELT, data integration, batch processing, and/or event-driven architectures, along with a strong understanding of distributed data workflows. - Deep knowledge of database internals, transaction management, concurrency, locking, isolation levels, indexing, and performance optimization. - Experience using AI coding assistants such as GitHub Copilot, Cursor, Claude Code, Augment, or similar tools to accelerate engineering work. - Familiarity with AI data technologies and concepts such as embeddings, vector databases, semantic search, retrieval-augmented generation, or AI-agent data infrastructure, with the ability and willingness to develop AI-enabled data tooling. - Experience with or exposure to modern data and cloud technologies such as PostgreSQL, Snowflake, Azure SQL, Cosmos DB, AWS data services, or comparable platforms. - Experience with infrastructure-as-code technologies such as Terraform, ARM, Bicep, or similar tools, as well as CI/CD, source control, database migrations, schema versioning, and deployment automation. - Familiarity with event streaming technologies such as Kafka and NoSQL or document databases is a plus. - Experience in payments, financial services, healthcare, benefits, or other highly regulated environments is preferred, as is experience supporting highly available, customer-facing or business-critical systems. - Oracle PL/SQL experience, hands-on RAG or semantic-search development, vector database expertise, and experience building AI-powered internal engineering tools are additional advantages. - Strong analytical, problem-solving, communication, collaboration, and technical leadership skills, with the ability to independently identify complex problems, make sound engineering decisions, and drive initiatives through completion. ### Benefits - Base salary range of $165,800 to $204,400 annually, with actual compensation determined based on qualifications, skills, competencies, experience, and proficiency for the role. - Eligibility for a quarterly or annual performance-based bonus, depending on the applicable compensation plan. - Comprehensive health, dental, and vision insurance options. - Retirement savings plan to support long-term financial wellbeing. - Paid time off for personal time, rest, and work-life balance. - Health Savings Account and Flexible Spending Account options. - Life insurance and disability insurance coverage. - Tuition reimbursement to support continued learning and professional development. - Fully remote work arrangement within the United States. - Opportunity to work on large-scale SQL Server modernization, cloud data platforms, and AI-enabled infrastructure. - Hands-on exposure to emerging technologies including generative AI, embeddings, vector databases, semantic search, RAG, agentic workflows, and AI-assisted engineering. - Significant technical ownership and influence over critical enterprise data platforms and engineering practices. - Collaboration with application engineering, SRE, operations, architecture, data, and AI/ML teams in a highly technical environment. - Inclusive workplace committed to diversity, belonging, equal opportunity, and reasonable accommodations.