Data Engineer (Databricks)

A leading commercial HVAC services platform with a large North American footprint, headquartered in Charlotte and operating across a highly distributed network of service businesses.

About the role

• Buys and supports reliable data pipelines that ingest information from multiple ERP systems, applications, databases, and operating locations into Databricks
• Strengthens connectivity between disconnected source environments and the organization’s centralized data platform
• Maintains current ingestion processes while supporting the transition toward a more scalable and standardized target-state architecture
• Partners with data architecture, analytics, infrastructure, and business teams to ensure data is accessible, accurate, and available for downstream use
• Reduces technical bottlenecks that impact enterprise reporting, analytics, and future AI initiatives

Responsibilities

• Develops and supports data pipelines within Databricks
• Connects Databricks to various source systems, including relational databases, ERP platforms, APIs, files, and on-premise environments
• Monitors pipeline performance, identifies failures or data-quality issues, and implements solutions that improve reliability and observability
• Works across data engineering, networking, infrastructure, MSP, and application teams to resolve complex connectivity issues
• Designs and implements ETL and ELT processes, manages pipeline dependencies, and ensures incremental loading

Requirements

• Strong hands-on experience developing, deploying, and supporting data pipelines within Databricks
• Experience with Databricks technologies and concepts such as Delta Lake, notebooks, workflows, jobs, clusters, and medallion architecture
• Proficiency with Python, PySpark, and SQL for data ingestion, transformation, and pipeline development
• Understanding of ETL and ELT design patterns, data orchestration, incremental loading, and pipeline dependency management
• Experience monitoring production pipelines, troubleshooting failures, and improving data quality, performance, and reliability
• Familiarity with cloud-based data environments and services within Azure, AWS, or a comparable platform
• Ability to work across data engineering, networking, infrastructure, MSP, and application teams to resolve complex connectivity issues

Qualifications

• Experience operating in a distributed, multi-location, or multi-ERP business environment is strongly preferred
• Background in HVAC, field services, construction, facilities services, or another decentralized service organization would be a plus

Skills

• Python
• PySpark
• SQL
• Databricks technologies (Delta Lake, notebooks, workflows, jobs, clusters, medallion architecture)
• ETL and ELT design patterns
• Data orchestration
• Incremental loading
• Pipeline dependency management

Benefits

• Opportunity to play a hands-on role in a major enterprise data modernization initiative
• Direct ownership of data connectivity, ingestion, monitoring, and pipeline management within a growing Databricks environment
• Chance to solve complex engineering challenges across a distributed, multi-location organization with numerous source systems
• Technical work that directly supports improved reporting, analytics, and AI capabilities across the business
• Exposure to a fast-moving, PE-backed organization where scalability, reliability, and practical execution are highly valued

Pay

• Competitive salary based on experience and qualifications

Schedule

• Hybrid (3 days on-site)