Senior AI Data EngineerLocation: Atlanta, GA (Hybrid)
Organization: RIB North America (RIB NAM)
OverviewAre you a Data Engineer passionate about being part of an Agentic AI transformation journey and playing a meaningful, hands-on role?
We are looking for a Senior AI Data Engineer to design and build scalable, secure, and high-performance data platforms that power next-generation AI applications. This role focuses on making enterprise data AI-ready by building robust data pipelines, enabling real-time data access, and supporting advanced use cases such as Generative AI, Retrieval-Augmented Generation (RAG), and intelligent applications.
You will collaborate closely with AI engineers, data engineers, and product teams to deliver reliable, production-grade data solutions within a modern Azure-based ecosystem.
Why Join Us- Work on cutting-edge AI / Agentic AI initiatives
- Build enterprise-scale AI-ready data platforms
- Collaborate with global teams across North America, Europe, and APAC
- Influence architecture, standards, and technical direction
Key Responsibilities- Build Data Ingestion Pipelines: Develop robust pipelines to extract data from various sources (databases, APIs, flat files) supporting SpecLink AI and related platforms
- Data Transformation & Processing: Implement scalable transformation and data quality processes to make raw data usable for AI workloads
- Data Loading & Indexing: Design and manage pipelines that load processed data into storage systems, search indices, and vector stores
- Real-Time & Incremental Processing: Enable streaming and near real-time data pipelines where required by AI systems
- Pipeline Orchestration & Automation: Implement scheduling, orchestration, and monitoring of data workflows
- Data Integration & APIs: Develop integration components and APIs to support real-time and on-demand data access
- Testing, Validation & Observability: Implement data quality checks, monitoring, and alerting for pipeline reliability
- Performance Optimization: Continuously optimize pipelines for performance, scalability, and cost efficiency
- Collaboration: Partner with global Data Engineering teams, R&D, and AI architects to implement RAG and AI data solutions
- Documentation & Maintainability: Document data models, pipelines, dependencies, and operational processes
- Operational Ownership: Monitor pipelines in production and handle post go-live maintenance and improvements
Required Experience & Skills- 6-10+ years of experience in Data Engineering or related roles
- Strong hands-on experience building end-to-end, production-grade data pipelines
- AI Tooling Exposure- Hands-on exposure to tools such as GitHub Copilot, Claude Code, Cursor, or similar AI-assisted development tools
- Data Engineering Fundamentals: SQL, data modelling (dimensional, normalized, Lakehouse)
- Azure Data Platform: Synapse, Data Factory / Fabric, Azure SQL, Cosmos DB
- Understanding of Modern Data Architecture: Lakehouse, Medallion architecture, Delta / Iceberg, batch + streaming
- AI Data Infrastructure: Vector databases, embeddings, RAG indexing, unstructured data pipelines
- Real-Time & Event-Driven Systems: Streaming, event-driven pipelines, asynchronous workflows
- Data Quality & Observability: Data freshness, retrieval quality, monitoring, SLAs, and cost tracking
- AI Evaluation Pipelines: Golden datasets, benchmarking, regression datasets
- Strong experience with MS SQL, Azure SQL, or Cosmos DB
- Exposure to building pipelines for AI/ML or Agentic AI use cases
- Solid understanding of incremental loads, CDC, and performance tuning
- Knowledge of Azure security (Managed Identity, RBAC, Key Vault)
Soft Skills:- Strong communication and stakeholder collaboration
- Ability to work in a fast-paced, cross-functional environment
- Ownership mindset with focus on business impact and delivery
Nice to Have- Experience with event-driven architecture and asynchronous processing
- Familiarity with CI/CD pipelines, DevOps practices, and Git-based workflows
