Data Engineer

Job Description

REQUIREMENTS

• Strong proficiency in Python and advanced SQL.
• Hands-on experience designing, building, and maintaining scalable data pipelines and data processing solutions.
• Strong experience with ETL/ELT processes, including data ingestion, transformation, integration, and orchestration.
• Practical experience with Apache Spark for large-scale data processing.
• Experience with Apache Kafka and event-driven/streaming data pipelines.
• Hands-on experience with database design, data modeling, optimization, and performance tuning.
• Practical experience with AWS cloud services and cloud-based data infrastructure.
• Experience with Apache Airflow for workflow orchestration and scheduling.
• Practical experience with Docker and containerized environments.
• Strong knowledge of Git and modern software development workflows.
• Solid understanding of data architecture, data quality, reliability, scalability, and data engineering best practices.
• Ability to write clean, efficient, testable, and maintainable code.
• Strong analytical, problem-solving, and critical-thinking skills with excellent attention to detail.
• Ability to work independently, prioritize effectively, and manage multiple tasks in a fast-paced environment.
• Languages: English, Russian
• Experience in fintech
• Strong analytical and problem-solving skills with excellent attention to detail.
• Ownership mindset, proactivity, and ability to work independently and manage priorities.
• Strong communication and collaboration skills with the ability to work effectively across teams.

RESPONSIBILITES

• Work with various data sources and integrate them into internal systems.
• Develop, optimize, and maintain ETL pipelines and data ingestion processes.
• Take ownership of data lakes, including their maintenance and documentation.
• Transform complex data structures into machine-readable formats.
• Design and maintain databases and data infrastructure.
• Work closely with other departments to verify the relevance, quality, and accuracy of data.
• Identify opportunities to improve the reliability and efficiency of existing data processes.

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