Job Title
Senior SQL Server & Data/AI Engineer
Department
IT/ERP
Reports to
Director
Job Category
Yearly
Job Description
Scope of Position
Reporting to the Director, the Senior SQL Server & Data/AI Engineer will own our SQL Server data layer end-to-end and help lay the foundations for our AI platform. Roughly 70% of your time will be on SQL Server, ETL, and data modelling: stabilizing, tuning, and improving what we already run in production. The rest will be spent designing data structures and pipelines for AI (vector store, RAG-style data access) and, if it makes sense, basic semantic models/SSAS.
Infrastructure/IT will manage servers, OS, and patching. You own schema, code, performance, ETL, and data models. You'll be expected to make architecture decisions, challenge assumptions, and push for robust, maintainable solutions.
Responsibilities
SQL Server Ownership (Data Layer)
- 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).
- Monitor and tune performance:
- Index and statistics strategy, query plans, blocking/deadlocks, resource usage.
- Maintain and troubleshoot:
- Replication, SQL Agent jobs, and other DB-level automation.
- Tables, views, indexes, constraints, and other DB objects for new features and integrations.
- Work with infrastructure/IT on:
- Capacity, patching windows, and DR, while ensuring the data layer supports those plans.
ETL & Data Workflows
- Own and improve existing ETL workflows (SSIS packages and/or custom ETL processes).
- Add proper logging, monitoring/alerting, and restart/recovery patterns.
- Document and rationalize data flows:
- Between SQL databases, ETL, upstream applications, and reporting/analytics.
- Implement validation checks, reconcile source vs target, and create repeatable fixes for recurring data issues.
SQL Development & Optimization
- Refactor and optimize stored procedures, functions, and queries to reduce runtime, resource usage, and complexity.
- Debug and resolve production data issues:
- Do real root-cause analysis (schema, ETL logic, upstream systems) rather than just patch symptoms.
- Establish and enforce SQL development standards:
- Naming conventions, error handling, transaction handling, deployment/version control for DB objects.
AI & Data Platform Enablement (Cloud-Friendly)
- Prepare and structure enterprise data for AI use cases (RAG, copilots, internal assistants, automation).
- Design and implement a vector-aware data store using pragmatic options (e.g., Azure SQL, managed vector stores, or similar), including:
- Schemas for documents, embeddings, and metadata.
- Ingestion and refresh pipelines that keep AI-ready data up to date and governed.
- Work with AI developers and stakeholders to define:
- What data AI can access, under which rules, and via which APIs/queries.
- When to use Azure or other cloud services (e.g., Azure OpenAI, managed search/vector services) and how they integrate with on-prem SQL.
Collaboration & (Light) Analytics Enablement
- Partner with application developers, BI/reporting, and business stakeholders to ensure data structures match actual usage.
- Translate business questions into reusable models/views/semantic layers where possible.
- If relevant and time allows, explore:
- Basic SSAS/semantic models or equivalent for stable reporting and as a source for AI tools.
Work Conditions
- Overtime hours with advanced notice may be required to meet project deadlines.
- Hybrid opportunities available - incumbent must commute to work.
Skills and Qualifications
- 5+ years hands-on experience as a SQL Server Developer / Database Engineer / Data Engineer with SQL Server as your main platform.
- Complex stored procedures, functions, and views.
- Solid query tuning using execution plans, index strategies, and statistics.
- Proven ETL / data integration experience:
- Designing, owning, and troubleshooting production ETL jobs (SSIS or similar tools, or custom script-based ETL).
- Relational data modelling, normalization vs denormalization, and the impact of design on performance and maintainability.
- Comfortable taking over legacy SQL and ETL:
- Reading messy code, simplifying it, and documenting what's actually happening.
- You can prioritize, communicate trade-offs, and defend good technical decisions to both technical and non-technical stakeholders.
- Clear written and verbal communication.
Nice to Have
- Experience with LLM/AI 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).
- SSIS, SSRS, SSAS, or similar ETL/reporting/analytics tools.
- Power BI modelling (DAX is a plus but not mandatory).
- Programming in .NET/C# or Python for data/ETL/AI integration and automation.
- Background in manufacturing, aerospace, or industrial environments with data flowing between ERP, PLM, MES, and quality systems.
Education
- A college diploma or university degree in computer science or software engineering.
