DATA ENGINEER

Market insights

Stack

Apply

Upload your CV — we will connect you with the employer directly through our pool.

Description

Key Responsibilities Design, develop and maintain scalable data pipelines and data ingestion frameworks for large-volume datasets. Develop data transformation and processing applications using Apache Spark, PySpark, Scala and Python . Build and optimize data pipelines using Azure Databricks, Azure Data Factory, AWS EMR and related cloud services. Work with Hadoop, HDFS, Hive, Snowflake, Teradata and Data Lake environments. Develop batch and real-time data processing solutions using Spark Structured Streaming and Kafka . Perform data extraction, transformation and loading across heterogeneous source and target systems. Develop and optimize Spark SQL, HiveQL and SQL queries for performance and cost efficiency. Design data models, partitioning strategies and scalable data storage architectures. Build and manage workflow orchestration using Apache Airflow . Implement CI/CD pipelines and automated testing using tools such as Jenkins, Docker, GitHub Actions and pytest . Troubleshoot data pipeline, performance and production issues and implement sustainable solutions. Collaborate with business stakeholders, architects and technology teams to understand requirements and deliver data engineering solutions. Ensure data quality, reliability, security and operational stability across enterprise data platforms. Required Skills 6+ years of experience in Data Engineering / Big Data Engineering . Strong hands-on experience with Apache Spark / PySpark . Strong programming skills in Python and

Employer contacts (email/phone/telegram) are hidden from the public preview — send your CV, and we will connect you directly.