Lead Data Reliability Engineer

Accountabilities:

• 
  Automate data tasks on GC
• PWork with data domain owners, data scientists and other stakeholders to ensure that data is consumed effectively on GC
• PDesign, build, secure and maintain data infrastructure, including data pipelines, databases, data warehouses, and data processing platforms on GC
• PMeasure and monitor the quality of data on GCP data platform
• sImplement 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

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Qualification

• s:
  Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science or related field, or equivalent practical experie
• nceProven experience as a Data Site Reliability Engineer or a similar role, with a strong focus on data infrastructure managem
• entGood understanding of SRE practi
• ce.Proficiency in data technologies, such as relational databases, data warehousing, big data platforms (e.g., Hadoop, Spark), data streaming (e.g., Kafka), and cloud services (e.g., AWS, GCP, Azur
• e).Strong programming skills in languages like Python (numpy, pandas, pyspark), Java (Core Java, Spark with Java, functional interface, lambda, java collections), or Scala, with experience in automation and scripti
• ng.Experience with containerization and orchestration tools like Docker and Kubernetes is a pl
• us.Experience with data governance(data plex), data security, and compliance best practices on
• GCPSolid understanding of software development methodologies and best practices, including version control (e.g., Git) and CI/CD pipelin
• es.Strong background in cloud computing and data-Intensive applications and services, with a focus on Google Cloud Platf
• ormExperience with data quality assurance and testing on
• GCPProficiency with GCP data services (BigQuery; Dataflow; Data Fusion; Dataproc; Cloud Composer; Pub/Sub; Google Cloud Stora
• ge)Strong understanding of logging and monitoring using tools such as Cloud Logging, ELK Stack, AppDynamics, New Relic, Splunk, e
• tc.Knowledge of AI and ML tools is a p
• lusGoogle Associate Cloud Engineer or Data Engineer certification is a p
• lus8+ years of experience in data engineering or data science on