TL;DR

Senior Big Data Engineer (Data Science/ML): Design, build, and maintain scalable data pipelines and ETL/ELT workflows using Python, PySpark, dbt, and AWS services with an accent on data ingestion, transformation, orchestration, and infrastructure automation. Focus on developing high-quality dbt models, implementing CI/CD, and ensuring data quality monitoring in a cross-functional agile environment.

Location: Remote, service region Eastern Europe

Company

%hirify_global% is a global digital product engineering company with 18,000+ experts across 39 countries, focused on building inspiring products, services, and experiences at scale.

What you will do

Design, build, and maintain scalable data pipelines using Python/PySpark and AWS services.
Develop, test, and deploy dbt models with unit tests and clear documentation.
Build and integrate data connectors (FTP, API, JDBC, etc.).
Implement data ingestion, transformation, and orchestration workflows using AWS Glue, AppFlow, Lake Formation, Transfer Family, MWAA, or Argo.
Use AWS CDK for infrastructure-as-code and deployment automation.
Apply best practices for CI/CD, version control, testing, and data quality monitoring.

Requirements

Service region: Eastern Europe
Strong hands-on experience with dbt, Python, and/or PySpark for ETL/ELT development.
Expertise in building connectors via FTP, API, JDBC, or other integration patterns.
Solid experience with AWS data services and workflow orchestration tools.
Experience with Git, CI/CD tools, and automated testing practices.
Good communication skills and ability to work in cross-functional agile teams.