About You
As a Data Engineer, you'll be responsible for developing, constructing, testing, and maintaining architectures such as databases and large-scale processing systems. You'll work closely with data scientists, analysts, and other stakeholders to understand their requirements and ensure smooth data flow within the organization. Your expertise in data warehousing, ETL (Extract, Transform, Load) processes, and containerization and orchestration technologies will be crucial in building and optimizing our data pipelines.
Your Day-to-Day
- Data Architecture Development:
- Design, develop, and maintain scalable data architectures, including databases, data warehouses, and data lakes.
- Evaluate and select appropriate technologies and tools for data storage, processing, and retrieval.
- Data Pipeline Implementation:
- Develop and optimize ETL processes to ingest, transform, and load data from various sources into our data infrastructure.
- Ensure data quality and integrity throughout the ETL process.
- Data Modeling:
- Design and implement data models to support analytics and reporting requirements.
- Collaborate with data scientists and analysts to understand their modeling needs and translate them into scalable data structures.
- Monitoring and Maintenance:
- Monitor data pipelines and infrastructure for performance, reliability, and security.
- Troubleshoot and resolve issues related to data processing, storage, and access.
- Documentation and Collaboration:
- Document data engineering processes, workflows, and best practices.
- Collaborate with cross-functional teams, including data scientists, analysts, software engineers, and business stakeholders, to understand requirements and deliver solutions.
Your Know-How
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- Proven experience as a Data Engineer or similar role, with a strong background in data warehousing and ETL.
- Proficiency in programming languages such as Python, Java, or Scala.
- Hands-on experience with relational and NoSQL databases (e.g., PostgreSQL, MySQL, MongoDB).
- Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform.
Learning Objective
- Teamwork and Corporate Environment: Gain hands-on experience working collaboratively in a corporate setting, understanding the dynamics of teamwork.
- Critical Thinking and Problem-Solving: Enhance critical thinking skills by addressing challenges and solving problems encountered during market research.
- Analytical Skills: Develop strong analytical skills through the collection and analysis of market data.
- Data Quality and Governance: Learn the importance of data quality and governance in maintaining accurate and reliable datasets.
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Last updated on Jun 10, 2024