Data Engineer – Cloud Platform

Job Description

Key Responsibilities :

– Design and implement robust data pipelines using GCP services such as BigQuery, Dataflow, Cloud Storage, and Dataproc to ingest, process, and transform large datasets for analytical and reporting purposes.

– Develop and maintain data models and schemas in BigQuery to ensure data quality, consistency, and accessibility for end-users.

– Automate data workflows and orchestrate data pipelines using Apache Airflow to improve efficiency and reduce manual effort.

– Optimize data processing performance and scalability by leveraging Apache Spark and PySpark to handle large-scale data transformations.

– Monitor data pipeline performance and troubleshoot issues to ensure data reliability and availability for critical business applications.

– Collaborate with data scientists and analysts to understand their data requirements and provide them with the necessary data infrastructure and tools to perform their analyses.

Required Skillset :

– Demonstrated ability to design, build, and maintain data pipelines on Google Cloud Platform (GCP) using services like BigQuery, Dataflow, and Cloud Storage.

– Proven expertise in data modeling, schema design, and data warehousing concepts to ensure data quality and accessibility.

– Strong proficiency in programming languages such as Python and SQL to develop data transformations and perform data analysis.

– Hands-on experience with Apache Spark and PySpark to process large-scale datasets efficiently.

– Experience with workflow orchestration tools such as Apache Airflow to automate data pipelines.

– Excellent problem-solving and communication skills to collaborate effectively with cross-functional teams.

– A Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field is preferred.

– Ability to adapt to a fast-paced and evolving environment, with a willingness to learn new technologies and techniques.

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

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