Hadoop Platform Engineer

Required Skills:

Platform Engineering:

• Cluster Management:

• Expertise in design, implement, and maintain Hadoop clusters in large volume, including components such as HDFS, YARN, and MapReduce.

• Collaborate with data engineers and data scientists to understand data requirements and optimize data pipelines.

• Administration and Monitoring:

• Experience in administering and monitoring Hadoop clusters to ensure high availability, reliability, and performance.

• Experience in troubleshooting and resolving issues related to Hadoop infrastructure, data ingestion, data processing, and data storage.

• Security Implementation:

• Experience in Implementing and managing security measures within Hadoop clusters, including authentication, authorization, and encryption.

• Backup and Disaster Recovery:

• Collaborate with cross-functional teams to define and implement backup and disaster recovery strategies for Hadoop clusters.

• Performance Optimization:

• Experience in optimizing Hadoop performance through fine-tuning configurations, capacity planning, and implementing performance monitoring and tuning techniques.

• Automation and DevOps Collaboration:

• Work with DevOps teams to automate Hadoop infrastructure provisioning, deployment, and management processes.

• Technology Adoption and Recommendations:

• Stay up to date with the latest developments in the Hadoop ecosystem.

• Recommend and implement new technologies and tools that enhance the platform.

• Documentation:

• Experience in documenting Hadoop infrastructure configurations, processes, and best practices.

• Technical Support and Guidance:

• Provide technical guidance and support to other team members and stakeholders.

Admin:

• User Interface Design:

• Relevant for designing interfaces for tools within the Hadoop ecosystem that provide self-service capabilities, such as Hadoop cluster management interfaces or job scheduling dashboards.

• Role-Based Access Control (RBAC):

• Important for controlling access to Hadoop clusters, ensuring that users have appropriate permissions to perform self-service tasks.

• Cluster Configuration Templates:

• Useful for maintaining consistent configurations across Hadoop clusters, ensuring that users follow best practices and guidelines.

• Resource Management:

• Important for optimizing resource utilization within Hadoop clusters, allowing users to manage resources dynamically based on their needs.

• Self-Service Provisioning:

• Pertinent for features that enable users to provision and manage nodes within Hadoop clusters independently.

• Monitoring and Alerts:

• Essential for monitoring the health and performance of Hadoop clusters, providing users with insights into their cluster's status.

• Automated Scaling:

• Relevant for automatically adjusting the size of Hadoop clusters based on workload demands.

• Job Scheduling and Prioritization:

• Important for managing data processing jobs within Hadoop clusters efficiently.

• Self-Service Data Ingestion:

• Applicable to features that facilitate users in ingesting data into Hadoop clusters independently.

• Query Optimization and Tuning Assistance:

• Relevant for providing users with tools or guidance to optimize and tune their queries when interacting with Hadoop-based data.

• Documentation and Training:

• Important for creating resources that help users understand how to use self-service features within the Hadoop ecosystem effectively.

• Data Access Control:

• Pertinent for controlling access to data stored within Hadoop clusters, ensuring proper data governance.

• Backup and Restore Functionality:

• Applicable to features that allow users to perform backup and restore operations for data stored within Hadoop clusters.

• Containerization and Orchestration:

• Relevant for deploying and managing applications within Hadoop clusters using containerization and orchestration tools.

• User Feedback Mechanism:

• Important for continuously improving self-service features based on user input and experience within the Hadoop ecosystem.

• Cost Monitoring and Optimization:

• Applicable to tools or features that help users monitor and optimize costs associated with their usage of Hadoop clusters.

• Compliance and Auditing:

• Relevant for ensuring compliance with organizational policies and auditing user activities within the Hadoop ecosystem.

Data Engineering:

• ETL (Extract, Transform, Load) Processes:

• Proficiency in designing and implementing ETL processes for ingesting, transforming, and loading data into Hadoop clusters.

• Experience with tools like Apache NiFi

• Data Modeling and Database Design:

• Understanding of data modeling principles and database design concepts.

• Ability to design and implement effective data storage structures in Hadoop.

• SQL and Query Optimization:

• Strong SQL skills for data extraction and analysis from Hadoop-based data stores.

• Experience in optimizing SQL queries for efficient data retrieval.

• Streaming Data Processing:

• Familiarity with real-time data processing and streaming technologies, such as Apache Kafka and Spark Streaming.

• Experience in designing and implementing streaming data pipelines.

• Data Quality and Governance:

• Knowledge of data quality assurance and governance practices.

• Implementing measures to ensure data accuracy, consistency, and integrity.

• Workflow Orchestration:

• Experience with workflow orchestration tools (e.g., Apache Airflow) to manage and schedule data processing workflows.

• Automating and orchestrating data pipelines.

• Data Warehousing Concepts:

• Understanding of data warehousing concepts and best practices.

• Integrating Hadoop-based solutions with traditional data warehousing systems.

• Version Control:

• Proficiency in version control systems (e.g., Git) for managing and tracking changes in code and configurations.

• Collaboration with Data Scientists:

• Collaborate effectively with data scientists to understand analytical requirements and support the deployment of machine learning models.

• Data Security and Compliance:

• Implementing security measures within data pipelines to protect sensitive information.

• Ensuring compliance with data security and privacy regulations.

• Data Catalog and Metadata Management:

• Implementing data catalog solutions to manage metadata and enhance data discovery.

• Enabling metadata-driven data governance.

• Big Data Technologies Beyond Hadoop:

• Familiarity with other big data technologies beyond Hadoop, such as Apache Flink or Apache Beam.

• Data Transformation and Serialization:

• Expertise in data serialization formats (e.g., Avro, Parquet) and transforming data between formats.

• Data Storage Optimization:

• Optimizing data storage strategies for cost-effectiveness and performance.

Desired Skills:

• Problem-Solving and Analytical Thinking:

• Strong analytical and problem-solving skills to troubleshoot complex issues in Hadoop clusters.

• Ability to analyze data requirements and optimize data processing workflows.

• Collaboration and Teamwork:

• Collaborative mindset to work effectively with cross-functional teams, including data engineers, data scientists, and DevOps teams.

• Ability to provide technical guidance and support to team members.

• Adaptability and Continuous Learning:

• Ability to adapt to changes in technology and industry trends within the Hadoop ecosystem and willingness to continuously learn and upgrade skills to stay current.

• Performance Monitoring and Tuning:

• Proactive approach to performance monitoring and tuning, ensuring optimal performance of Hadoop clusters.

• Ability to analyze and address performance bottlenecks.

• Security Best Practices:

• knowledge of security best practices within the Hadoop ecosystem.

• Capacity Planning:

• Skill in capacity planning to anticipate and scale Hadoop clusters according to data processing needs.

• Automation and Scripting:

• Strong scripting skills for automation (e.g., Python, Ansible) beyond shell scripting. Familiarity with configuration management tools for infrastructure automation.

• Monitoring and Observability:

• Experience in setting up comprehensive monitoring and observability tools for Hadoop clusters. Ability to proactively identify and address potential issues.

• Networking Skills:

• Understanding of networking concepts relevant to Hadoop clusters.

Skills:

• Technical Proficiency:

• Experience with Hadoop and Big Data technologies, including Cloudera CDH/CDP, Data Bricks, HD Insights, etc.

• Strong understanding of core Hadoop services such as HDFS, MapReduce, Kafka, Spark, Hive, Impala, HBase, Kudu, Sqoop, and Oozie.

• Proficiency in RHEL Linux operating systems, databases, and hardware administration.

• Operations and Design:

• Operations, design, capacity planning, cluster setup, security, and performance tuning in large-scale Enterprise Hadoop environments.

• Scripting and Automation:

• Proficient in shell scripting (e.g., Bash, KSH) for automation.

• Security Implementation:

• Experience in setting up, configuring, and managing security for Hadoop clusters using Kerberos with integration with LDAP/AD.

• Problem Solving and Troubleshooting:

• Expertise in system administration and programming skills for storage capacity management, debugging, and performance tuning.

• Collaboration and Communication:

• Collaborate with cross-functional teams, including data engineers, data scientists, and DevOps teams.

• Provide technical guidance and support to team members and stakeholders.