Senior Data Engineer

Driven by transformative digital technologies and trends, we are RIB and we’ve made it our purpose to propel the industry forward and make engineering and construction more efficient and sustainable. Built on deep industry expertise and best practice, and with our people at the heart of everything we do, we deliver the world's leading end-to-end lifecycle solutions that empower our industry to build better.

With a steadfast commitment to innovation and a keen eye on the future, RIB comprises over 2,500 talented individuals who extend our software’s reach to over 100 countries worldwide. We are experienced experts and professionals from different cultures and backgrounds and we collaborate closely to provide transformative software products, innovative thinking and professional services to our global market. Our strong teams across the globe enable sustainable product investment and enhancements, to keep our clients at the cutting-edge of engineering, infrastructure and construction technology.

We know our people are our success – join us to be part of a global force that uses innovation to enhance the way the world builds.

Find out more at RIB Careers.

Senior AI Data Engineer

Location: Atlanta, GA (Hybrid)
Organization: RIB North America (RIB NAM)

Overview

Are 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-drive