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Develop data processing pipelines using programming languages like Java and Python to extract, transform, and load log data
Implement scalable, fault-tolerant solutions for data ingestion, processing, and storage.
Support systems engineering lifecycle activities for data engineering deployments, including requirements gathering, design, testing, implementation, operations, and documentation.
Automating platform management processes through Ansible or other scripting tools/languages
Troubleshooting incidents impacting the log data platforms
Collaborate with cross-functional teams to understand data requirements and design scalable solutions that meet business needs.
Develop training and documentation materials
Support log data platform upgrades including coordinating testing of upgrades with users of the platform
Gather and process raw data from multiple disparate sources (including writing scripts, calling APIs, writing SQL queries, etc.) into a form suitable for analysis
Participate in on-call rotations to address critical issues and ensure the reliability of data engineering systems
Experience General
Ability to troubleshoot and diagnose complex issues
Able to demonstrate experience supporting technical users and conduct requirements analysis
Can work independently with minimal guidance & oversight
Experience with IT Service Management and familiarity with Incident & Problem management
Highly skilled in identifying performance bottlenecks, identifying anomalous system behavior, and resolving root cause of service issues.
Demonstrated ability to effectively work across teams and functions to influence design, operations, and deployment of highly available software
Knowledge of standard methodologies related to security, performance, and disaster recovery
Required Technical Expertise
Expertise in languages such as Java, Python. Implementation knowledge in data processing pipelines using programming languages like Java and Python to extract, transform, and load (ETL) data
Create and maintain data models, ensuring efficient storage, retrieval, and analysis of large datasets
Troubleshoot and resolve issues related to data processing, storage, and retrieval.
3-5 years of experience in designing, developing, and deploying data lakes using AWS native services (S3, Glue (Crawlers, ETL, Catalog), IAM, Terraform, Athena)
Experience in development of systems for data extraction, ingestion and processing of large volumes of data
Experience with data pipeline orchestration platforms
Experience in Ansible/Terraform/Cloud Formation scripts and Infrastructure as Code scripting is required
Implement version control and CI/CD practices for data engineering workflows to ensure reliable and efficient deployments
Proficiency in implementing monitoring, logging, and alerting solutions for data infrastructure (e.g., Prometheus, Grafana)
Proficiency in distributed Linux environments
Preferred Technical Experience
Familiarity with data streaming technologies such as Kafka, Kinesis, spark streaming, etc.
Knowledge of cloud platforms (prefer AWS) and container + orchestration technologies
Experience with AWS OpenSearch, Splunk
Experience with common scripting and query languages
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