Data Operations Engineer

Akkodis is seeking a Senior Data Operations Engineer for one of our clients in Charlotte, North Carolina.

Rate Range: $55.00/hour to $65.00/hour; the rate may be negotiable based on experience, education, geographic location, and other factors.

We are seeking an experienced Senior Data Ops Engineer to lead the design, automation, optimization, and operational management of modern enterprise data platforms built on Databricks and AWS cloud technologies. This role is responsible for delivering scalable, secure, and highly available data solutions that support enterprise analytics, data engineering, and AI/ML initiatives.

As a senior technical leader, you will drive Data Ops best practices, Infrastructure as Code (IaC), platform governance, CI/CD automation, and operational excellence across the data ecosystem. You will partner closely with architecture, analytics, engineering, and business teams to build and maintain reliable data platforms that power data-driven decision making.

This is a hybrid position requiring 4 days per week onsite and 1 day per week remote.

• Lead the design, development, optimization, and operational support 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 administer Databricks clusters to ensure performance, scalability, reliability, and cost efficiency.
• Implement Delta Lake best practices, including partitioning, schema evolution, compaction, optimization, and performance tuning.
• Manage Unity Catalog governance, including access controls, lineage, auditing, security standards, and compliance requirements.
• Design and support Medallion/Lakehouse architectures across Bronze, Silver, and Gold data layers.
• Develop monitoring, observability, alerting, and troubleshooting processes for Databricks workloads and platform services.
• Support enterprise analytics, reporting, and AI/ML workloads running on Databricks.
• Develop and maintain scalable data ingestion, transformation, and integration pipelines utilizing Python, PySpark, SQL, AWS Glue, and AWS-native services.
• Integrate structured, semi-structured, unstructured, and streaming data from a variety of enterprise and cloud-based sources.
• Design and implement real-time and event-driven data processing solutions leveraging AWS Kinesis, Firehose, and related streaming technologies.
• Collaborate with cross-functional teams to deliver high-quality, enterprise-grade data solutions.

• Lead Infrastructure as Code (IaC) initiatives using Terraform to provision and manage Databricks environments and supporting cloud infrastructure.
• Automate environment provisioning, deployments, configuration management, and operational workflows.
• Design, implement, and maintain CI/CD pipelines supporting application, code, and infrastructure deployments.
• Maintain source control repositories and standardized Dev Ops processes that enable mature Data Ops practices.
• Drive platform consistency, governance, and deployment standards across development, test, and production environments.

• Ensure compliance with enterprise data governance, privacy, security, and regulatory requirements.
• Implement data quality controls, lineage tracking, auditability, and operational governance frameworks.
• Establish monitoring standards, operational procedures, platform support models, and best practices.
• Provide technical leadership, mentorship, and guidance to engineering teams on Data Ops, cloud, and Databricks platform best practices.

• 8+ years of experience in Data Engineering, Platform Engineering, Data Ops, or related disciplines.
• 5+ years of hands-on experience designing and supporting enterprise-scale Databricks environments.
• Extensive experience with PySpark, Spark SQL, Python, SQL, and distributed data processing frameworks.
• Deep knowledge of Delta Lake, Databricks Workflows, Unity Catalog, cluster administration, and performance optimization.
• Proven experience implementing Lakehouse/Medallion architectures in Databricks.
• Strong expertise with Terraform and Infrastructure as Code (IaC) practices.
• Experience building and maintaining CI/CD pipelines and modern Dev Ops/Data Ops frameworks.
• Strong knowledge of AWS cloud services, including AWS Glue, Kinesis, Firehose, S3, and IAM.
• Experience implementing data governance, observability, security, and monitoring frameworks in enterprise environments.
• Excellent communication, collaboration, problem-solving, and leadership skills.

• Experience supporting enterprise AI/ML platforms and advanced analytics workloads.
• Databricks, AWS, Terraform, or related cloud certifications.
• Experience working in Agile delivery environments.
• Familiarity with data platform modernization and cloud transformation initiatives.

• Production-ready Databricks data pipelines and…