Вакансия Middle Big Data Software Engineer

Skills

• AWS
• Azure
• GCP
• Python
• Golang
• dbt
• SQL
• BigQuery
• Redshift
• Snowflake
• Git
• Apache Spark
• DataBricks

Описание вакансии

Our client is a leading managed WordPress hosting platform, providing businesses with the speed, security, and reliability they need to thrive online. Their platform simplifies and automates many of the complex tasks associated with managing a WordPress website, allowing businesses to focus on creating great content and growing their audience.

If you are

• Experienced in Data Engineering with at least 3 years in the field
• Proficient in one or more cloud platforms (AWS, Azure, or GCP), preferably GCP
• Skilled in Python or Go, another scripting language
• Well-versed in DBT and SQL to deliver data assets
• Having SQL debugging skills and query performance optimization
• Knowledgeable of data warehouse technologies like BigQuery (preferred), Redshift, or Snowflake
• Proficient in working with Git
• Comfortable communicating daily with English-speaking software engineers on the client side
• Familiar with Big Data frameworks such as Spark or Databricks (would be a plus)

And you want to

• Develop and maintain data replication pipelines in a cloud environment using Python, Go, or third-party vendors such as Fivetran
• Organize and normalize data through data modeling using dbt
• Develop automated test cases to ensure continued high-quality data and protect against changes
• Contribute to the client's CI/CD practice to automate the delivery of high-quality code
• Monitor, troubleshoot, and address alerts from data jobs
• Create and maintain system, data, and architectural documentation related to data assets such as ERDs
• Collaborate with cross-functional teams to contribute to data warehousing and analytics solutions
• Advocate for maintaining high-quality and highly maintainable data assets through best practices and standards
• Submit and review code contributions and drive adoption of best practices and hygiene for healthy data models
• Identify and develop process improvements to enhance the Data and Analytics Engineering practice