Data Engineer / Junior–Middle

This is a remote position.

We are looking for a Junior/Middle Data Engineer with a strong focus on Python, SQL, and building data pipelines.

The main responsibility of this role is connecting to various data sources, extracting data via REST APIs, databases, files, and third-party platforms, processing it, and loading it into a data warehouse for further analytics and BI reporting.

We are looking for someone who understands that a data pipeline is not just a script, but a stable process with logging, error handling, retries, monitoring, and data quality checks.

Location: Kazakhstan/Remote

Requirements

Required Skills & Experience

• Strong knowledge of Python and practical experience using it in data engineering tasks.
  
• Experience building data pipelines for loading, processing, and transforming data.
  
• Experience working with various data sources: REST APIs, databases, CSV/Excel/JSON files, cloud storage, and third-party platforms.
  
• Hands-on experience integrating with REST APIs: authentication, pagination, rate limits, retries, timeout handling, and error handling.
  
• Understanding of how to build fault-tolerant pipelines.
  
• Experience setting up incremental data loading and handling partial loads.
  
• Ability to work with JSON and semi-structured data.
  
• Strong SQL knowledge: JOINs, CTEs, aggregates, and window functions.
  
• Experience loading data into databases or data warehouses such as PostgreSQL, BigQuery, Snowflake, Redshift, MS SQL, or similar systems.
  
• Understanding of ETL/ELT approaches.
  
• Experience with logging, monitoring, and basic troubleshooting of pipelines.
  
• Experience working with Git.
  

Nice to Have

• Experience working with dbt: models, sources, tests, documentation, incremental models.
  
• Experience with Spark / PySpark.
  
• Experience using orchestration tools such as Airflow, Prefect, Dagster, or similar.
  
• Experience implementing data quality checks: freshness, duplicates, completeness, consistency.
  
• Experience working with cloud storage: AWS S3, Google Cloud Storage, Azure Blob Storage.
  
• Experience with Docker.
  
• Understanding of dimensional modelling principles: fact/dimension tables, star schema, data marts.
  
• Experience optimizing SQL queries and pipelines.
  

Bonus Points

• Experience working with BI tools such as Power BI, Tableau, Looker, QuickSight, Domo, or similar.
  
• Experience preparing datasets for BI reporting and analytical data marts.
  
• Basic understanding of cloud platforms such as GCP, AWS, or Azure.
  
• Experience with CI/CD for data projects.
  
• Ability to document pipeline logic, data sources, and transformations clearly.
  

Benefits