An exciting opportunity has arisen for a skilled Data Engineer to join our team and contribute to the development of our data infrastructure.
The successful candidate will be responsible for designing, building, and maintaining large-scale data systems, including data pipelines, data warehouses, and data lakes.
This is a remote opportunity with the potential to become a permanent position.
Key Responsibilities
* Design, build, and maintain large-scale data systems.
* Design and implement data warehouses using tools such as Amazon Redshift, Google BigQuery, and Snowflake.
* Develop and maintain data pipelines using tools such as Apache Beam, Apache Spark, and AWS Glue.
* Develop and maintain data lakes using tools such as Apache Hadoop, Apache Spark, and Amazon S3.
* Work with data architects to design and implement data models and data architectures.
* Collaborate with data scientists to develop and deploy machine learning models and data products.
* Ensure data quality and integrity by developing and implementing data validation and data cleansing processes.
* Collaborate with other teams to ensure that data systems meet the business's needs.
* Stay up-to-date with new technologies and trends in data engineering and make recommendations for adoption.
Requirements:
* 5+ years of experience in data engineering or a related field.
* 2 - 4 years of experience in Ruby products, including Ruby on Rails framework.
* 5+ years of experience with programming languages such as Python, Java, and Scala.
* 3+ years of experience with data modeling and data architecture.
* 3+ years of experience with data engineering tools such as Apache Beam, Apache Spark, AWS Glue, Amazon Redshift, Google BigQuery, and Snowflake.
* Strong experience with data warehousing and data lakes.
* Strong experience with data validation and data cleansing.
* Strong collaboration and communication skills.
* Bachelor's degree in Computer Science, Engineering, or a related field.
Nice to Have:
* Experience with machine learning and data science.
* Experience with cloud-based data platforms such as AWS, GCP, or Azure.
* Experience with containerization using Docker and Kubernetes.
* Experience with agile development methodologies such as Scrum or Kanban.
* Experience with data governance and data security.