Data Reliability Engineer II (dRE)Role Overview
A crucial role in CME's Cloud transformation, the dRE II will be aligned to data product pods ensuring that our data infrastructure is reliable, scalable, and efficient as the GCP data footprint expands rapidly.
Accountabilities
Automate data tasks on Google Cloud Platform (GCP).
Work with data domain owners, data scientists, and other stakeholders to ensure that data is consumed effectively on GCP.
Design, build, secure, and maintain data infrastructure, including data pipelines, databases, data warehouses, and data processing platforms on GCP.
Measure and monitor the quality of data on GCP data platforms.
Implement robust monitoring and alerting systems to proactively identify and resolve issues in data systems.
Respond to incidents promptly to minimize downtime and data loss.
Develop automation scripts and tools to streamline data operations and make them scalable to accommodate growing data volumes and user traffic.
Optimize data systems to ensure efficient data processing, reduce latency, and improve overall system performance.
Collaborate with data and infrastructure teams to forecast data growth and plan for future capacity requirements.
Ensure data security and compliance with data protection regulations.
Implement best practices for data access controls and encryption.
Collaborate with data engineers, data scientists, and software engineers to understand data requirements, troubleshoot issues, and support data-driven initiatives.
Continuously assess and improve data infrastructure and data processes to enhance reliability, efficiency, and performance.
Maintain clear and up-to-date documentation related to data systems, configurations, and standard operating procedures.
Qualifications
Education: Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, or a related field, or equivalent practical experience.
Professional Experience: Experience as a Site Reliability Engineer or a similar role with a strong focus on data infrastructure management.
Methodologies: Understanding of Site Reliability Engineering (SRE) practices.
Data Technologies: Proficiency in data technologies such as relational databases, data warehousing, big data platforms (e.g., Hadoop), data streaming (e.g., Kafka), and cloud services (e.g., AWS, GCP, Azure).
Programming Skills: Programming skills in languages like Python (NumPy, pandas, PySpark), Java (Core Spark with Java, functional interface, collections), or Scala with experience in automation and scripting.
Infrastructure Tools: Experience with containerization and orchestration tools like Docker and Kubernetes is a plus.
Compliance: Experience with data governance, data security, and compliance best practices on GCP.
Software Development: Understanding of software development methodologies and best practices, including version control (e.g., Git) and CI/CD pipelines.
Cloud Computing: Any experience in cloud computing and data-intensive applications and services, ideally Google Cloud Platform (GCP) would be highly beneficial.
Quality Assurance: Experience with data quality assurance and testing on GCP.
GCP Data Services: Proficiency with GCP data services (BigQuery, Dataflow, Data Fusion, Dataproc, Cloud Composer, Pub/Sub, Google Cloud Storage).
Monitoring Tools: Understanding of logging and monitoring using tools such as Cloud Logging, ELK Stack, AppDynamics, New Relic, and Splunk.
Advanced Tech: Knowledge of AI and ML tools is a plus.
Certifications: Google Associate Cloud Engineer or Data Engineer certification is a plus.
Domain Specifics: Experience in data engineering or data science.
Ongoing Employee Development Training/Certification
Hybrid Working
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