Job VC
Data Engineer (Middle)
Technologies
Description
🚀We are looking for a
Middle+/Senior Data Engineer
to join a long-term data platform project. The role is focused on building, maintaining, and optimizing scalable data pipelines, data warehouses, and cloud-based data platforms for high-load business environments.
The ideal candidate has strong hands-on experience with Python, SQL, Spark/PySpark, cloud data services, and modern ETL/ELT pipelines. Experience with AWS is highly important, while Databricks, Snowflake, BigQuery, dbt, Airflow, Kafka/Kinesis, and Terraform are strong advantages.
🧩
Responsibilities:
Design, build, and maintain scalable batch and near-real-time data pipelines.
Develop and optimize ETL/ELT workflows using Python, SQL, Spark/PySpark, Airflow, dbt, or similar tools.
Work with cloud-based data platforms and services, especially AWS.
Build and support data lakes, lakehouse architectures, and data warehouses.
Integrate data from APIs, databases, SaaS tools, files, and streaming/event-based sources.
Optimize data processing performance, query execution, partitioning, and storage costs.
Ensure data quality, reliability, monitoring, logging, and validation across pipelines.
Collaborate with engineers, analysts, BI teams, data scientists, and business stakeholders.
Support documentation, data modeling, and data dictionary creation where needed.
Contribute to CI/CD, infrastructure automation, and cloud data platform improvements.
🔑Requirements:
3-5 years of experience in Data Engineering or closely related roles.
Strong hands-on experience with Python, SQL, and Spark/PySpark.
Experience building and maintaining production-grade ETL/ELT pipelines.
Strong understanding of data warehouses, data lakes, lakehouse architectures, and data modeling.
Experience with AWS data services such as S3, Glue, Lambda, Athena, EMR, Redshift, Firehose, Kinesis, SQS, or EventBridge.
Experience with orchestration and transformation tools such as Airflow, dbt, Prefect, Dagster, or similar.
Experience with at least one modern data platform: Databricks, Snowflake, BigQuery, Redshift, or similar.
Understanding of data quality, monitoring, observability, and performance optimization.
Experience with Git, CI/CD, Docker, Terraform, AWS CDK, or other DevOps/IaC tools.
Ability to work independently, communicate clearly, and take ownership of delivery.
English level: Upper-Intermediate or higher.
🧩Nice to Have:
Experience with Kafka, Kinesis, Pub/Sub, or other streaming/event-driven technologies.
Experience with Delta Lake, Iceberg, Hudi, or other lakehouse table formats.
Experience with Kubernetes.
Experience in high-load, fintech, healthcare, telecom, eCommerce, SaaS, or analytics-heavy products.
Experience with BI/reporting tools such as Tableau, Looker, Metabase, Power BI, or similar.
Exposure to AI/ML or LLM-related data workflows.
Experience with GCP or Azure data services.
🎯We Offer:
Paid vacation and sick leave
We provide the equipment you need to work comfortably (if required)
We cover 50% of courses if you want to grow professionally
Regular salary reviews based on your performance
Competitive salary, depending on your experience
Always on-time payments — no delays
A normal, supportive team without unnecessary bureaucracy
Middle+/Senior Data Engineer
to join a long-term data platform project. The role is focused on building, maintaining, and optimizing scalable data pipelines, data warehouses, and cloud-based data platforms for high-load business environments.
The ideal candidate has strong hands-on experience with Python, SQL, Spark/PySpark, cloud data services, and modern ETL/ELT pipelines. Experience with AWS is highly important, while Databricks, Snowflake, BigQuery, dbt, Airflow, Kafka/Kinesis, and Terraform are strong advantages.
🧩
Responsibilities:
Design, build, and maintain scalable batch and near-real-time data pipelines.
Develop and optimize ETL/ELT workflows using Python, SQL, Spark/PySpark, Airflow, dbt, or similar tools.
Work with cloud-based data platforms and services, especially AWS.
Build and support data lakes, lakehouse architectures, and data warehouses.
Integrate data from APIs, databases, SaaS tools, files, and streaming/event-based sources.
Optimize data processing performance, query execution, partitioning, and storage costs.
Ensure data quality, reliability, monitoring, logging, and validation across pipelines.
Collaborate with engineers, analysts, BI teams, data scientists, and business stakeholders.
Support documentation, data modeling, and data dictionary creation where needed.
Contribute to CI/CD, infrastructure automation, and cloud data platform improvements.
🔑Requirements:
3-5 years of experience in Data Engineering or closely related roles.
Strong hands-on experience with Python, SQL, and Spark/PySpark.
Experience building and maintaining production-grade ETL/ELT pipelines.
Strong understanding of data warehouses, data lakes, lakehouse architectures, and data modeling.
Experience with AWS data services such as S3, Glue, Lambda, Athena, EMR, Redshift, Firehose, Kinesis, SQS, or EventBridge.
Experience with orchestration and transformation tools such as Airflow, dbt, Prefect, Dagster, or similar.
Experience with at least one modern data platform: Databricks, Snowflake, BigQuery, Redshift, or similar.
Understanding of data quality, monitoring, observability, and performance optimization.
Experience with Git, CI/CD, Docker, Terraform, AWS CDK, or other DevOps/IaC tools.
Ability to work independently, communicate clearly, and take ownership of delivery.
English level: Upper-Intermediate or higher.
🧩Nice to Have:
Experience with Kafka, Kinesis, Pub/Sub, or other streaming/event-driven technologies.
Experience with Delta Lake, Iceberg, Hudi, or other lakehouse table formats.
Experience with Kubernetes.
Experience in high-load, fintech, healthcare, telecom, eCommerce, SaaS, or analytics-heavy products.
Experience with BI/reporting tools such as Tableau, Looker, Metabase, Power BI, or similar.
Exposure to AI/ML or LLM-related data workflows.
Experience with GCP or Azure data services.
🎯We Offer:
Paid vacation and sick leave
We provide the equipment you need to work comfortably (if required)
We cover 50% of courses if you want to grow professionally
Regular salary reviews based on your performance
Competitive salary, depending on your experience
Always on-time payments — no delays
A normal, supportive team without unnecessary bureaucracy