Bigdata and Hadoop Developer

Functional Skills

Functional Skills are essential skills that help individuals perform tasks effectively in everyday life, work, and education.

Hadoop Ecosystem Expertise

Spark Proficiency

Data Warehousing and ETL

SQL and NoSQL Databases

Programming Skills (Java, Python, Scala)

Data Modeling and Schema Design

Cloud Computing (AWS, Azure, GCP)

Real-time Data Processing

Data Security and Governance

Linux and Shell Scripting

Version Control (Git)

Testing and Debugging

Soft Skills

Communication, teamwork, and critical thinking to ensure seamless project execution.

Analytical Thinking

Problem-Solving

Communication

Collaboration

Adaptability

Attention to Detail

Time Management

Critical Thinking

Technical Skills

Technical Skills are specialized abilities and knowledge needed to perform specific tasks or functions.

Hadoop Ecosystem

Apache Spark

Data Warehousing

NoSQL Databases

Programming Languages

Data Ingestion Tools

Linux Fundamentals

Cloud Computing

Data Visualization

Real-Time Data Processing

Version Control (Git)

Testing and Debugging

Duties and Responsiblities

Task

95%

Design and develop scalable Big Data solutions using Hadoop and related technologies like Spark and Hive.

92%

Write MapReduce jobs for data extraction, transformation, and loading (ETL) from various data sources into Hadoop.

90%

Implement data ingestion techniques using tools like Apache Flume and Apache Kafka for real-time data processing.

88%

Optimize Hadoop cluster performance by tuning configurations and monitoring resource utilization using tools like Ganglia or Ambari.

85%

Develop and maintain data pipelines using workflow management tools such as Apache Oozie or Apache Airflow.

82%

Create and manage data models and schemas in Hadoop-based data warehouses like Hive or Impala.

80%

Implement data security and access control measures within the Hadoop ecosystem using Kerberos and other security frameworks.

78%

Troubleshoot and resolve issues related to Hadoop cluster performance, data processing, and data quality.

75%

Collaborate with data scientists and analysts to understand their data requirements and provide them with the necessary data sets.

72%

Stay up-to-date with the latest Big Data technologies and trends, and evaluate their potential impact on the organization.

70%

Write and maintain documentation for Hadoop-based solutions, including data pipelines, data models, and cluster configurations.

68%

Participate in code reviews and ensure adherence to coding standards and best practices for Big Data development.

Work Activities

95%

Collect and transfer data from diverse sources into Hadoop for processing.

92%

Administer and optimize Hadoop clusters for performance and reliability.

90%

Develop and execute MapReduce jobs for large-scale data processing.

88%

Design efficient data models for storage and retrieval in Hadoop.

85%

Optimize Hadoop jobs and configurations for maximum throughput.

82%

Implement security measures to protect sensitive data within Hadoop.

80%

Identify and resolve issues within the Hadoop environment.

78%

Integrate Hadoop with data warehouses for comprehensive analytics.

75%

Implement solutions for real-time data processing using Hadoop technologies.

72%

Establish and enforce data governance policies within the Hadoop ecosystem.

70%

Work with data scientists and analysts to support their data needs.

Detailed Work Profile

Design and develop scalable big data solutions using Hadoop and related technologies.

Implement data ingestion pipelines to collect and process large datasets from various sources.

Write MapReduce jobs and Spark applications for data processing and analysis.

Optimize Hadoop cluster performance and ensure data security.

Develop and maintain data warehouses and data lakes using Hadoop ecosystem tools.

Collaborate with data scientists and business analysts to understand data requirements.

Implement data governance policies and ensure data quality.

Troubleshoot and resolve issues related to Hadoop infrastructure and data pipelines.

Stay updated with the latest Hadoop ecosystem technologies and best practices.

Automate data processing tasks using scripting languages like Python or Shell.

Monitor Hadoop cluster resources and performance using monitoring tools.

Design and implement data models for efficient data storage and retrieval.

Interests