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Senior Exceutive
Your Job 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.
Must-have Qualifications, Skills & Experience
  • 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.
Good to have Skills & Experience
  • 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.
Senior Exceutive
Data Engineering
3-6 years
Noida

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.

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