Senior SQL Server & Data/AI Engineer

Description

• Improve data quality:
• Partner with application developers, BI/reporting, and business stakeholders to ensure data structures match actual usage.
• Between SQL databases, ETL, upstream applications, and reporting/analytics.
• Debug and resolve production data issues:
• Design and implement a vector-aware data store using pragmatic options (e.g., Azure SQL, managed vector stores, or similar), including:
• Designing, owning, and troubleshooting production ETL jobs (SSIS or similar tools, or custom script-based ETL).
• Capacity, patching windows, and DR, while ensuring the data layer supports those plans.
• Add proper logging, monitoring/alerting, and restart/recovery patterns.
• Tables, views, indexes, constraints, and other DB objects for new features and integrations.
• Refactor and optimize stored procedures, functions, and queries to reduce runtime, resource usage, and complexity.
• Translate business questions into reusable models/views/semantic layers where possible.
• Own and improve existing ETL workflows (SSIS packages and/or custom ETL processes).
• Work with AI developers and stakeholders to define:
• Document and rationalize data flows:
• Implement validation checks, reconcile source vs target, and create repeatable fixes for recurring data issues.
• Establish and enforce SQL development standards:
• Own several production SQL Server instances and ~10 databases from a data perspective (schemas, code, performance, reliability, security at the DB level, backups/restore strategies).
• Prepare and structure enterprise data for AI use cases (RAG, copilots, internal assistants, automation).
• Reading messy code, simplifying it, and documenting what’s actually happening.

Requirements

• Basic SSAS/semantic models or equivalent for stable reporting and as a source for AI tools.
• Index and statistics strategy, query plans, blocking/deadlocks, resource usage.
• When to use Azure or other cloud services (e.g., Azure OpenAI, managed search/vector services) and how they integrate with on-prem SQL.
• Stabilize ETL:
• Schemas for documents, embeddings, and metadata.
• Ingestion and refresh pipelines that keep AI-ready data up to date and governed.
• Naming conventions, error handling, transaction handling, deployment/version control for DB objects.
• Do real root-cause analysis (schema, ETL logic, upstream systems) rather than just patch symptoms.
• 5+ years hands-on experience as a SQL Server Developer / Database Engineer / Data Engineer with SQL Server as your main platform.
• Replication, SQL Agent jobs, and other DB-level automation.
• Proven ETL / data integration experience:
• Relational data modelling, normalization vs denormalization, and the impact of design on performance and maintainability.
• Solid grasp of:
• Clear written and verbal communication.
• Comfortable taking over legacy SQL and ETL:
• Experience with LLM/AI systems:
• SSIS, SSRS, SSAS, or similar ETL/reporting/analytics tools.
• A college diploma or university degree in computer science or software engineering.
• Strong T-SQL skills:
• Background in manufacturing, aerospace, or industrial environments with data flowing between ERP, PLM, MES, and quality systems.
• RAG-style architectures, embeddings, prompt design, chatbots, or internal copilots.
• Hands-on exposure to vector databases / vector stores or search platforms with vector capabilities (cloud or self-hosted).
• Power BI modelling (DAX is a plus but not mandatory).
• You can prioritize, communicate trade-offs, and defend good technical decisions to both technical and non-technical stakeholders.
• Solid query tuning using execution plans, index strategies, and statistics.
• Complex stored procedures, functions, and views.
• Programming in .NET/C# or Python for data/ETL/AI integration and automation.

Benefits

• Hybrid opportunities available – incumbent must commute to work.

Training + Development

Interview process

Visa Sponsorship

Security clearance