Data Operations Engineer

We are seeking both a DataOps Engineer and a Senior DataOps Engineer to support enterprise-scale data platform initiatives focused on Databricks and AWS cloud technologies. These positions will play a critical role in building scalable, reliable, and highly governed data environments supporting analytics, AI/ML, and enterprise integration initiatives.

This is an excellent opportunity to join a growing data and AI engineering organization working on modern cloud-native architecture and next-generation data platform initiatives.

Responsibilities

Data Platform Engineering

• Build and maintain scalable ETL/ELT pipelines using Databricks, PySpark, Spark SQL, and Python
• Support batch and streaming data workloads
• Configure and optimize Databricks clusters for performance and scalability
• Implement Delta Lake best practices and medallion/lakehouse architecture
• Support Unity Catalog governance, lineage, and security controls
• Develop integrations using AWS services including Glue, Kinesis, Firehose, and S3

Infrastructure & Automation

• Support Infrastructure as Code (IaC) initiatives using Terraform
• Build and maintain CI/CD pipelines for Databricks deployments
• Automate deployment and operational workflows
• Monitor and troubleshoot platform operations and data pipelines

Governance & Operational Excellence

• Implement data quality, observability, and monitoring frameworks
• Support enterprise governance, security, and compliance standards
• Create operational documentation and support procedures
• Participate in Agile sprint ceremonies and cross-functional collaboration

Do you have experience in Spark?, Qualifications - DataOps Engineer

• 5+ years of experience in Data Engineering, DataOps, or Platform Engineering
• 3+ years of hands-on Databricks experience
• Strong Python, PySpark, Spark SQL, and SQL skills
• Experience with AWS cloud services and ETL pipelines
• Exposure to Terraform and CI/CD pipelines preferred

Qualifications - Senior DataOps Engineer

• 8+ years of experience in Data Engineering, Platform Engineering, or DataOps
• 5+ years of enterprise Databricks experience
• Deep expertise with Delta Lake, Unity Catalog, and lakehouse architecture
• Strong Terraform and Infrastructure as Code experience
• Experience implementing CI/CD and operational governance frameworks
• Proven leadership and mentoring capabilities

Preferred Experience

• Databricks Workflows
• Streaming/event-driven architecture
• Data governance and lineage tools
• Enterprise-scale cloud data platforms
• AI/ML workload support

Interested?

We'd love to connect with experienced DataOps and Databricks professionals looking to work on cutting-edge enterprise data initiatives., The company is seeking experienced DataOps and Data Engineering professionals who are passionate about building scalable cloud data solutions, automation frameworks, and enterprise data platforms that drive innovation and operational excellence., * Do you have at least 5 years of experience in Data Engineering, DataOps, or Platform Engineering?

• Are you able to start immediately?
• Do you have strong Python, PySpark, Spark SQL, and SQL skills?

Work Location: Hybrid remote in Charlotte, NC 28205

RL Enterprise Recruiting & Staffing is partnering with an innovative and rapidly growing automotive manufacturer focused on building next-generation electric vehicles and advanced mobility solutions in the United States. Our client is investing heavily in modern cloud technologies, enterprise data platforms, AI/ML capabilities, and digital transformation initiatives to support large-scale manufacturing, operational analytics, and connected vehicle technologies. Their engineering organization is building highly scalable, cloud-native data environments leveraging Databricks, AWS, and modern DataOps practices to support enterprise-wide analytics and business intelligence initiatives. This organization offers the opportunity to work on cutting-edge technology projects within a collaborative, fast-paced, and forward-thinking environment alongside highly skilled engineering, analytics, and platform teams.