Big Data Engineer

Data Ingestion: Design and implement data ingestion processes to acquire data from various sources, including databases, sensors, logs, and external APIs.

Data Storage: Create and manage data storage solutions that can handle massive datasets, often using technologies like Hadoop Distributed File System (HDFS) and NoSQL databases.

Data Processing: Develop data processing pipelines and ETL (Extract, Transform, Load) workflows to clean, transform, and aggregate data for analysis.

Big Data Technologies: Work with big data technologies and frameworks like Apache Hadoop, Spark, Kafka, and others to process and analyze data at scale.

Data Modeling: Build data models and schemas to support efficient data retrieval and query performance.

Data Security: Implement security measures to protect sensitive data and ensure compliance with data privacy regulations.

Scalability and Performance: Optimize data infrastructure for scalability, performance, and reliability, and troubleshoot issues as they arise.

Monitoring and Maintenance: Set up monitoring tools and systems to track the health and performance of data infrastructure components.

Data Integration: Integrate data from various sources, both structured and unstructured, to create a unified view of the data.

Documentation: Maintain documentation on data engineering processes, workflows, and infrastructure configurations.

Collaboration: Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and deliver the necessary data solutions.

Automation: Implement automation where possible to streamline data workflows and reduce manual intervention.

Qualifications and Skills:

Education: A bachelors or masters degree in computer science, data engineering, or a related field is often required.

Programming Skills: Proficiency in programming languages such as Python, Java, Scala, and scripting languages for data processing.

Big Data Technologies: In-depth knowledge of big data technologies and frameworks,