Fabric Data Engineer

arrivia•Scottsdale, AZ

10h•Hybrid

About The Position

Build the modern data backbone a global travel-rewards business runs on, work hands-on with the newest Microsoft Fabric and AI tooling, and coach the engineers coming up behind you. About the Role arrivia is looking for an experienced Fabric Data Engineer to take ownership of complex data pipeline architectures inside the Microsoft Fabric ecosystem. You will design and optimize enterprise-scale data solutions, set the technical standards the team works to, and mentor junior engineers along the way. This work matters right now. You will help migrate an existing on-premise relational database into our modern cloud environment and play an instrumental role in decommissioning legacy data-warehouse systems. The pace is accelerating, and your pipelines are what let the business rely on accurate, up-to-date reports and dashboards. Your impact reaches across the company. You will partner with analysts, data scientists, and business stakeholders to turn complex requirements into reliable, performant, well-governed data platforms, and the standards you set will shape how the whole team builds. It is also a genuinely modern role. At arrivia, we are committed to AI-driven innovation, and you will work alongside the latest AI technologies, using LLMs, Model Context Protocol servers, and AI-assisted tools to accelerate delivery and raise engineering quality. You will also get flexible and hybrid work, certification support, and a clear path to grow into Senior, Lead, and Principal roles.

Requirements

3 to 5 years in data engineering, ETL and ELT development, or a related analytics engineering role.
Strong SQL across T-SQL and Spark SQL, and strong Python with PySpark.
Several years working with Apache Spark and lakehouse data at scale, writing PySpark and Spark SQL notebooks against large datasets, with real performance-tuning experience.
Solid grounding in lakehouse architecture, data warehousing, dimensional modeling, and data vault methodology.
Hands-on experience with Microsoft Fabric or a comparable cloud data platform such as Azure Synapse or Databricks.
Experience with real-time and streaming data at scale, including event-driven architectures and tools like Azure Event Hubs or Kafka.
Familiarity with vector search, embeddings, and Retrieval-Augmented Generation patterns, plus hands-on use of LLMs and AI-assisted development tools.
Strong CI/CD habits across Fabric deployment pipelines, Git, and automated testing.
Demonstrated experience mentoring junior engineers and leading technical initiatives.
A bachelor's degree in a related field, or equivalent practical experience.

Nice To Haves

Working knowledge of Scala is a plus.
Microsoft Certified: Fabric Data Engineer Associate (DP-700) is highly preferred.

Responsibilities

Architect and optimize end-to-end pipelines with Microsoft Fabric Data Factory, Dataflows Gen2, and PySpark and Spark SQL notebooks, built for scale and performance.
Lead Lakehouse and Warehouse design on the medallion (Bronze, Silver, Gold) pattern, and set the best practices the team follows.
Help move on-premise relational data into OneLake and retire legacy data-warehouse systems, accelerating the shift to a modern cloud platform.
Build low-latency streaming pipelines with Fabric Eventstream from sources such as Azure Event Hubs, IoT Hub, and custom applications.
Write highly optimized T-SQL, Spark SQL, and PySpark, and own incremental loads, refresh scheduling, and SLA monitoring so pipelines stay dependable.
Design pipelines that support Retrieval-Augmented Generation, including chunking, embeddings, and vector search, and use LLMs and MCP servers to speed up and improve how the team works.
Champion governance through sensitivity labels, role-based access controls, and cataloging in Microsoft Purview.
Drive CI/CD with Fabric deployment pipelines, Git branching strategies, and automated testing for data assets.
Maintain Power BI semantic models where needed, so enterprise reporting stays consistent and accurate.
Coach Fabric Data Engineer I team members through code reviews, pair programming, and knowledge sharing, and help lead architectural reviews and continuous improvement.