Job
Description
About CloudKeeper CloudKeeper is a cloud cost optimization partner that combines the power of group buying & commitments management, expert cloud consulting & support, and an enhanced visibility & analytics platform to reduce cloud cost & help businesses maximize the value from AWS, Microsoft Azure, & Google Cloud. A certified AWS Premier Partner, Azure Technology Consulting Partner, Google,Cloud Partner, and FinOps Foundation Premier Member, CloudKeeper has helped 400+ global companies save an average of 20% on their cloud bills, modernize their cloud set-up and maximize value all while maintaining flexibility and avoiding any long-term commitments or cost. CloudKeeper hived off from TO THE NEW, digital technology services company with 2500+ employees and an 8-time GPTW winner. 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. 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.ing technologies. 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.
