Senior Data Engineer

Вимоги

• 5+ years of professional experience in Data Engineering
• Strong Python and SQL development skills for pipeline development and optimisation
• Proficiency in Apache Spark / PySpark, including query optimisation and performance tuning
• Hands-on experience with Databricks (preferred) or Snowflake
• Experience with at least one major cloud provider: Azure (preferred), AWS, or GCP
• Experience with stream processing technologies (Kafka, Spark Structured Streaming)
• Solid understanding of ETL/ELT patterns, data modelling (dimensional, Data Vault), and data warehousing
• Experience with orchestration tools (Apache Airflow, Azure Data Factory, or equivalent)
• Knowledge of Infrastructure as Code (Terraform or equivalent)
• Understanding of production-grade system requirements: reliability, scalability, observability, and performance
• Upper-Intermediate English level

WILL BE A PLUS:

• Familiarity with RAG pipeline design and LLM integration patterns
• Knowledge of data governance frameworks and tools (Unity Catalog, Apache Atlas, or similar)
• Experience with dbt for data transformation and modelling
• Familiarity with MLflow, Feature Stores, or ML platform integration

Професійний профайл

• Self-driven and proactive in identifying improvements
• Comfortable working in a fast-paced, innovative environment
• Strong problem-solving mindset with attention to detail
• Open to experimenting with emerging technologies and approaches

Обов'язки

• Design and build scalable, cloud-native data platforms from greenfield to production
• Implement near-real-time ingestion pipelines using event-driven patterns
• Define and enforce platform standards, including Data Lake / Lakehouse principles, medallion architecture, and data contracts
• Refactor and optimise existing Spark and PySpark scripts for performance and maintainability
• Introduce best practices for code quality, testing, and CI/CD across data pipelines
• Drive adoption of AI tooling and agentic workflows within the data engineering team
• Ensure data quality, observability, and reliability across all pipelines and platforms
• Develop self-service tooling and microservices to simplify platform usage for other teams