Overview

The Principal Data Engineer is a senior technical leader within AvidXchange's Data Engineering organization responsible for architecting, building, and scaling modern data platforms. In this role, you will drive the migration of legacy Azure SQL Server workloads to Databricks, design real-time streaming pipelines with Apache Kafka, enable AI and agentic capabilities such as Databricks Genie, and define the long-term data architecture strategy. You will partner closely with Software Engineering, Product, Architecture, DevOps, and Analytics teams to deliver secure, high-performing, and reliable data solutions at enterprise scale.

What You'll Do

Data Platform Architecture & Modernization

· Lead the design and implementation of scalable, cloud-native data architectures on Databricks (Delta Lake, Unity Catalog, Lakehouse patterns).

· Own and execute the migration strategy from legacy Azure SQL Server to Databricks, including schema translation, ETL/ELT re-platforming, data validation, and cutover planning.

· Define data modeling standards (medallion architecture, star/snowflake schemas) and ensure consistency across all pipelines and domains.

· Evaluate and recommend tools, frameworks, and platforms to support long-term data strategy and organizational goals.

· Collaborate with Solution and Enterprise Architects to review and approve new data architecture designs.

Streaming & Real-Time Data Engineering

· Architect and implement Kafka-based streaming pipelines for real-time data ingestion, transformation, and delivery.

· Design event-driven architectures and streaming topologies using Kafka Streams, ksqlDB, or Spark Structured Streaming on Databricks.

· Establish patterns for schema management (Confluent Schema Registry), consumer group strategy, offset management, and dead-letter queuing.

· Ensure streaming pipelines meet SLA requirements for latency, throughput, and fault tolerance.

Optimization, Quality & Standards

· Debug and optimize Spark jobs, Delta Lake tables, and SQL workloads for performance, cost efficiency, and maintainability.

· Lead code reviews focused on senior engineers to enforce standards, best practices, and technical quality.

· Manage pipeline quality, data models, and CI/CD delivery workflows; guide teams on continuous improvement.

· Promote strong data management practices — data quality, lineage, observability, and governance.

· Identify opportunities to improve service delivery methods, processes, and resource utilization.

Leadership, Mentorship & Strategy

· Mentor data engineers at all levels, with particular emphasis on developing senior talent.

· Establish and evolve data engineering standards, best practices, and management of technical debt.

· Stay current on data platform trends (Databricks releases, Kafka ecosystem, open table formats) and contribute to long-term architectural vision.

· Develop plans for data security, disaster recovery, backup, business continuity, and archiving across the Lakehouse.

AI, Agentic Capabilities & Intelligent Data Products

· Design and enable AI and agentic capabilities on the Databricks platform, including Databricks Genie for natural language data exploration and self-service analytics.

· Architect data foundations — clean, governed, well-documented Delta tables — that power Genie spaces, AI/BI dashboards, and LLM-driven data agents.

· Collaborate with ML and AI teams to build and maintain feature stores, vector stores, and retrieval-augmented generation (RAG) pipelines on Databricks