Technology and Engineering | L2​/L3 Data Platform Support Engineer - Arlington, VA Hybrid Arlington, Virginia,

Job : | 1515 North Courthouse Road Arlington Virginia 22201 United States | Full-time

As a Senior Associate Full Stack Data Platform Support Engineer, you will be responsible for supporting and maintaining business-critical data platforms and cloud-native applications running on AWS. You will ensure the availability, stability, and performance of batch and real-time data processing systems by proactively monitoring production environments, troubleshooting complex issues, performing root cause analysis, and driving timely incident resolution. You will collaborate closely with SRE, Infrastructure, Development, and Business teams to support production deployments, optimize platform performance, improve operational efficiency, and implement automation to enhance system reliability and customer experience.

• Provide L2 production support for enterprise data platforms running on AWS, ensuring high availability and reliability of business-critical applications.
• Monitor and support Big Data applications built on EMR (Spark/Hadoop), Kafka, Apache NiFi, EKS, Aurora PostgreSQL, Document DB, Couchbase, and S3.
• Investigate, troubleshoot, and resolve production incidents, application failures, performance bottlenecks, and infrastructure issues within defined SLAs.
• Perform root cause analysis (RCA) for recurring issues and implement permanent fixes in collaboration with engineering teams.
• Monitor batch and streaming data pipelines, ensuring timely completion of critical workflows and resolving data processing failures.
• Support workflow orchestration using Oozie and assist in maintaining scheduling dependencies and operational stability.
• Monitor and troubleshoot Kafka producers, consumers, topics, partitions, and message processing, including Kafka lag analysis.
• Support Kubernetes (Amazon EKS) workloads, monitor pod health, node utilization, and application deployments.
• Monitor EMR cluster health, auto-scaling, resource utilization, Spark job execution, and optimize performance when required.
• Support database administration activities for Aurora PostgreSQL, Amazon Document DB, and Couchbase, including query analysis and performance troubleshooting.
• Execute production deployments, configuration changes, and release validations through Jenkins and Harness CI/CD pipelines.
• Develop and maintain operational dashboards, monitoring alerts, and health checks using enterprise monitoring tools such as Dynatrace and Cloud Watch.
• Perform capacity planning, performance analysis, and recommend infrastructure optimization opportunities to improve system stability and reduce cloud costs.
• Work closely with cross-functional teams including SRE, Infrastructure, Development, Product, and Business teams during incident resolution and planned maintenance activities.
• Participate in on-call support, production releases, disaster recovery exercises, and major incident management.
• Identify automation opportunities to reduce manual operational effort and improve platform reliability.
• Ensure compliance with operational procedures, security standards, and change management processes

• 6+ years of overall IT experience.
• 4+ years in Data Engineering and Big Data technologies.
• Strong experience developing Spark applications using Scala/Python.
• Experience building cloud-native applications on Kubernetes/EKS.
• Hands-on experience with Kafka-based streaming architecture.
• Experience designing scalable ETL/data ingestion pipelines using Apache NiFi.
• Good understanding of distributed systems, Hadoop ecosystem, and data lake architecture.
• Experience with relational and No
  
  SQL databases.
• Experience in production support, performance optimization, and troubleshooting.
• Knowledge of CI/CD, Infrastructure as Code, and cloud security best practices.

• Experience with microservices architecture and REST APIs.
• Experience with Kubernetes deployments and containerized applications.
• Knowledge of EMR cluster administration and performance tuning.
• Exposure to Infrastructure as Code (Terraform/Cloud Formation).
• AWS Certification (Associate or Professional) is preferred.
• Excellent analytical, debugging, and communication skills.

• Experience with containerization and orchestration tools such as Docker, Kubernetes.
• Knowledge of machine learning frameworks and data science workflows.
• Certifications in Big Data technologies (e.g., AWS Certified Big Data – Specialty, Cloudera Certified Professional).
• Experience in financial services, e-commerce, or supply chain…