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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # DataOps Engineer (Local to Charlotte, NC) - **Company:** Bertrandt US Inc - **Location:** Charlotte, NC, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Unity 3d, Agile Methodology, Artificial Intelligence, Amazon Web Services, Amazon S3, Data Analysis, Cloud Computing, Configuration Management, Continuous Integration, Information Engineering, Data Governance, Data Integration, Extract Transform Load (ETL), Distributed Computing Environment, Monitoring of Systems, Apache Hive, Identity and Access Management, Python (Programming Language), Performance Tuning, Cloud Services, DataOps, Software Deployment, SQL Databases, Data Streaming, Enterprise Data Management, Data Processing, Data Ingestion, Infrastructure as Code (IaC), Data Layers, Data Lakes, Pyspark, Infrastructure Automation Frameworks, Information Technology, Data Lineage, Deployment Automation, AWS Glue, Video Streaming, Terraform, Software Version Control, Data Pipelines, Databricks - **Published:** May 24, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=80ffddefbdd0be19 ## About the Role Do you have experience in Version control?, 5+ years of experience in Data Engineering, DataOps, or Platform Engineering. * 3+ years of hands-on experience with Databricks in enterprise environments. * Strong proficiency in Python, PySpark, Spark SQL, and SQL. * Hands-on experience with Databricks Workflows, Delta Lake, and Unity Catalog. * Experience building and supporting scalable ETL/ELT pipelines and distributed data processing solutions. * Working knowledge of Terraform and Infrastructure as Code (IaC) practices. * Experience with AWS cloud services including AWS Glue, Kinesis, Firehose, S3, and IAM. * Understanding of data governance, security, monitoring, and operational best practices. ## Description The DataOps Engineer will support the design, implementation, automation, and operational management of enterprise data platforms leveraging Databricks and AWS cloud services. The role will focus on building scalable and reliable data pipelines, supporting Databricks platform operations, and implementing DataOps and Infrastructure as Code (IaC) best practices to enable secure and efficient data processing across the enterprise. The engineer will work closely with data engineering, analytics, AI/ML, and platform teams to support data integration, operational monitoring, governance, and deployment automation initiatives. Core Responsibilities Databricks Platform & DataOps * Develop, maintain, and optimize ETL/ELT pipelines within Databricks using PySpark, Spark SQL, and Databricks Workflows. * Support batch and streaming data processing workloads within Databricks environments. * Configure and manage Databricks clusters to support scalability, reliability, and cost optimization. * Implement Delta Lake best practices including partitioning, schema evolution, optimization, and performance tuning. * Support Unity Catalog administration including access controls, governance policies, lineage, and auditing. * Contribute to medallion/lakehouse architecture implementations across bronze, silver, and gold data layers. * Monitor and troubleshoot Databricks jobs, workflows, pipelines, and cluster operations using platform monitoring and observability tools. * Support enterprise analytics, reporting, and AI/ML workloads running on Databricks. Data Engineering & Integration * Develop and maintain scalable data ingestion and transformation pipelines using Python, PySpark, SQL, AWS Glue, and related AWS services. * Integrate structured, semi-structured, unstructured, and streaming data from multiple enterprise and cloud data sources. * Support real-time and event-driven integrations using AWS Kinesis, Firehose, and related streaming technologies. * Collaborate with cross-functional teams to deliver scalable and reliable enterprise data solutions. Infrastructure Automation & CI/CD * Support Infrastructure as Code (IaC) initiatives using Terraform for provisioning and managing Databricks and cloud infrastructure components. * Assist with automating deployment processes, configuration management, and operational workflows. * Support CI/CD pipelines for Databricks code deployments and infrastructure automation. * Maintain version-controlled repositories and deployment automation processes following DataOps best practices. Governance, Security & Operations * Support implementation of data governance, privacy, security, and compliance controls across the platform. * Implement and maintain data quality checks, lineage tracking, and operational monitoring processes. * Contribute to operational documentation, runbooks, and support procedures. * Participate in troubleshooting, root cause analysis, and continuous platform improvement initiatives. Deliverables * Production-ready Databricks ETL/ELT pipelines and workflows. * Scalable batch and streaming data integration solutions. * Terraform scripts and Infrastructure as Code templates for platform provisioning. * Monitoring dashboards and operational alerts for Databricks workloads and pipelines. * Data lineage, metadata, and operational documentation. * CI/CD deployment automation and operational support documentation. * Weekly status reports and participation in Agile sprint ceremonies. ## Related Videos - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) - [How we built an AI-powered code reviewer in 80 hours](https://www.wearedevelopers.com/videos/1511-how-we-built-an-ai-powered-code-reviewer-in-80-hours) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Tips, Techniques, and Common Pitfalls Debugging Kafka](https://www.wearedevelopers.com/videos/838-tips-techniques-and-common-pitfalls-debugging-kafka) ## Related Articles - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [DevOps Engineer Salary [2023]](https://www.wearedevelopers.com/magazine/203-devops-engineer-salary-2023) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Best US AI Conferences for CTOs in 2026: Build vs. Buy, Vendor Evaluation, and Peer Intelligence](https://www.wearedevelopers.com/magazine/736-best-us-ai-conferences-for-ctos-in-2026-build-vs-buy-vendor-evaluation-and-peer-intelligence) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)