Senior Data Ops Engineer (Local to Charlotte, NC)

Job Description Job Description Description: Ready to drive the future? As part of the global Bertrandt Group, our team of innovators tackles cutting-edge projects across ADAS, Autonomous Driving, Electric Mobility, and Manufacturing Support, transforming complex issues into sustainable, connected solutions. With the strength of a global network of over 14,500 colleagues in 50+ locations, Bertrandt US combines deep expertise in Electronics, Product Engineering, Physical, and Production & After Sales. Join us in engineering tomorrow’s mobility today. General Benefits: Complete and comprehensive benefits package including Med/Dent/Vision Employer paid STD/LTD/Life 401k Retirement program Generous paid vacation/sick/holidays Creativity encouraged in a fun, friendly work environment __________________________________________________________________________________________________________________________________ *This position can be hybrid-remote within general Charlotte area * *Start date is approximately 6/8/26 * *Local candidates only The Senior DataOps Engineer will lead the design, implementation, automation, and operational support of enterprise-scale data platforms primarily leveraging Databricks and AWS cloud services. The role will focus on building highly scalable, reliable, and governed data pipelines, optimizing Databricks platform operations, and implementing Infrastructure as Code (IaC) and DataOps best practices across the enterprise data ecosystem. The engineer will serve as a senior technical resource responsible for Databricks platform engineering, operational excellence, workload optimization, governance implementation, automation, and CI/CD enablement supporting analytics, AI/ML, and enterprise data initiatives. Core Responsibilities Databricks Platform Engineering & DataOps • Lead the design, development, optimization, and operational management of enterprise-scale ETL/ELT pipelines within Databricks. • Build and maintain scalable batch and streaming data pipelines using PySpark, Spark SQL, Delta Lake, and Databricks Workflows. • Configure, optimize, and manage Databricks clusters for performance, scalability, reliability, and cost efficiency. • Implement and enforce Delta Lake best practices including partitioning, schema evolution, compaction, optimization, and performance tuning. • Administer and manage Unity Catalog, including governance policies, access controls, lineage, auditing, and data security standards. • Design and support medallion/lakehouse architecture patterns across Bronze, Silver, and Gold data layers. • Implement operational monitoring, observability, alerting, and troubleshooting processes for Databricks jobs, workflows, clusters, and platform services. • Support enterprise AI/ML and analytics workloads running on Databricks. Cloud Data Engineering & Integration • Develop and maintain scalable data ingestion and transformation pipelines using Python, PySpark, SQL, AWS Glue, and cloud-native AWS services. • Integrate structured, semi-structured, unstructured, and streaming data from enterprise and cloud-based data sources. • Implement real-time and event-driven data processing using AWS Kinesis, Firehose, and related streaming technologies. • Collaborate with architecture, analytics, AI/ML, and platform teams to deliver enterprise-grade data solutions. Infrastructure Automation & DevOps • Lead Infrastructure as Code (IaC) implementation using Terraform for provisioning and managing Databricks workspaces, clusters, jobs, permissions, and related cloud infrastructure. • Automate environment provisioning, deployment processes, configuration management, and operational workflows. • Implement and maintain CI/CD pipelines supporting Databricks code deployments, infrastructure automation, and platform operations. • Maintain version-controlled repositories and DevOps processes supporting enterprise DataOps practices. • Drive platform standardization, operational governance, and deployment consistency across environments. Governance, Security & Operational Excellence • Ensure compliance with enterprise data governance, privacy, security, and regulatory standards. • Implement data quality validation, lineage tracking, auditability, and operational controls. • Establish operational best practices, platform standards, monitoring frameworks, and support procedures. • Provide technical leadership, mentorship, and guidance for DataOps and Databricks engineering practices. Deliverables • Production-ready Databricks ETL/ELT pipelines and workflows. • Optimized and governed Databricks platform environments. • Terraform modules and Infrastructure as Code automation templates. • Monitoring, observability, and operational dashboards for Databricks workloads and pipelines. • Enterprise data models, lineage documentation, and operational runbooks. • CI/CD pipelines and deployment automation frameworks. • Weekly status reports and participation in Agile sprint ceremonies. Requirements: Required Skills & Experience •8+ years of experience in Data Engineering, Platform Engineering, or DataOps roles. •5+ years of hands-on experience with Databricks in enterprise-scale environments. •Strong expertise in PySpark, Spark SQL, Python, SQL, and distributed data processing EEO-Statement: Bertrandt US is committed to fostering an inclusive and diverse workplace. We provide equal employment opportunities to all employees and applicants and strictly prohibit discrimination or harassment of any kind. We consider all qualified candidates without regard to race, color, religion, age, sex, national origin, disability, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable federal, state, or local laws