Build and maintain reliable data pipelines using Python and SQL. Integrate REST APIs, databases, files, cloud storage, and third-party platforms; process and load data into warehouses for analytics and BI. Implement incremental and fault-tolerant loading, logging, monitoring, retries, error handling, and data quality checks. Work with ETL/ELT, Git, and potentially dbt, Spark, orchestration tools, cloud platforms, Docker, dimensional modeling, and BI datasets.
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
- A variety of projects: trust us, you won’t be bored.
- A sane schedule: we focus on tasks, not hours—but showing up at noon every day isn’t exactly smiled upon.
- A team that values expertise and humor: yes, we occasionally crack jokes about SQL—don’t worry if you don’t laugh right away.
- Choose your adventure: Dive deep into a single, large-scale project or opt for a “discovery” mode, collaborating with multiple global clients across different domains. You can get hands-on with cutting-edge data stacks for anything from gaming and dating to skyscraper construction and nuclear energy. If variety is what you crave, you’ll find it here.
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