Data Engineer

Position Overview:

We are looking for an experienced and driven Data Engineer to join our team. The ideal candidate will have a strong foundation in big data technologies, particularly Spark, and a basic understanding of Scala to design and implement efficient data pipelines. As a Data Engineer at CloudKeeper, you will be responsible for building and maintaining robust data infrastructure, integrating large datasets, and ensuring seamless data flow for analytical and operational purposes.

Key Responsibilities:

• Design, develop, and maintain scalable data pipelines and ETL processes to collect, process, and store data from various sources.
  
• Work with Apache Spark to process large datasets in a distributed environment, ensuring optimal performance and scalability.
  
• Develop and optimize Spark jobs and data transformations using Scala for large-scale data processing.
  
• Collaborate with data analysts and other stakeholders to ensure data pipelines meet business and technical requirements.
  
• Integrate data from different sources (databases, APIs, cloud storage, etc.) into a unified data platform.
  
• Ensure data quality, consistency, and accuracy by building robust data validation and cleansing mechanisms.
  
• Use cloud platforms (AWS, Azure, or GCP) to deploy and manage data processing and storage solutions.
  
• Automate data workflows and tasks using appropriate tools and frameworks.
  
• Monitor and troubleshoot data pipeline performance, optimizing for efficiency and cost-effectiveness.
  
• Implement data security best practices, ensuring data privacy and compliance with industry standards.
  
• Stay updated with new data engineering tools and technologies to continuously improve the data infrastructure.

Required Qualifications:

• 4- 6 years of experience required as a Data Engineer or an equivalent role
• Strong experience working with Apache Spark with Scala for distributed data processing and big data handling.
  
• Basic knowledge of Python and its application in Spark for writing efficient data transformations and processing jobs.
  
• Proficiency in SQL for querying and manipulating large datasets.
  
• Experience with cloud data platforms, preferably AWS (e.g., S3, EC2, EMR, Redshift) or other cloud-based solutions.
  
• Strong knowledge of data modeling, ETL processes, and data pipeline orchestration.
  
• Familiarity with containerization (Docker) and cloud-native tools for deploying data solutions.
  
• Knowledge of data warehousing concepts and experience with tools like AWS Redshift, Google BigQuery, or Snowflake is a plus.
  
• Experience with version control systems such as Git.
  
• Strong problem-solving abilities and a proactive approach to resolving technical challenges.
  
• Excellent communication skills and the ability to work collaboratively within cross-functional teams.

Preferred Qualifications:

• Experience with additional programming languages like Python, Java, or Scala for data engineering tasks.
  
• Familiarity with orchestration tools like Apache Airflow, Luigi, or similar frameworks.
  
• Basic understanding of data governance, security practices, and compliance regulations.