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Data Engineer

3 weeks ago

Remote, Czech Republic AVENGA Full time
  • Proven experience as a Data Engineer with strong hands-on skills in Python development
  • Solid background in building and managing ETL pipelines using AWS technologies
  • Experience with CI/CD and DevOps tools – GitLab, DataOps.live, Docker
  • Strong knowledge of dbt and modern data transformation techniques
  • Familiarity with automation tools and scripting languages (e.g., Bash)
  • Expertise with AWS data ecosystem (Glue, Lambda, Fargate, S3, Step Functions, RDS, etc.)
  • Experience supporting and working with cross-functional teams in a dynamic environment
  • Knowledge of data governance, security, and working with distributed cloud architectures
  • Excellent problem-solving skills and ability to work independently as well as collaboratively
,[Design, build, and maintain optimal data pipeline architecture in a cloud-native environment, Assemble large and complex data sets that align with business and technical requirements, Identify and implement internal process improvements: automate workflows, optimize delivery, and redesign for scalability, Develop CI/CD pipelines, primarily using GitLab and DataOps practices, Model and implement data transformations using dbt (Data Build Tool) with scripting and algorithmic logic, Automate and streamline operations using Python, Bash, Docker, and GitLab, Build and maintain ETL solutions using AWS services (e.g., S3, Glue, RDS, Lambda, Fargate, Step Functions), Write clean, efficient, and reusable Python code for data processing and automation, Develop analytics tools and services that leverage the pipeline to provide insights into key business metrics, Collaborate with stakeholders across Executive, Product, Data, and Design teams to support data infrastructure needs, Ensure data security and separation across international boundaries and AWS regions, Create and maintain data tools and frameworks to support the work of analysts and data scientists, Continuously explore new technologies and approaches to improve functionality, performance, and scalability of data systems] Requirements: SQL, ETL, Data modeling, Snowflake, dbt, GitLab, Python, AWS, Docker, Scripting language, Bash, Glue, AWS Lambda, AWS Fargate, AWS S3, Amazon RDS, Security, Cloud, Python, Vault Additionally: Sport subscription, Training budget, Private healthcare, International projects.