Senior Data Engineer

• Design and build scalable cloud-native data platforms from greenfield to production
• Design and implement near-real-time ingestion pipelines using event-driven patterns
• Define and enforce data platform standards, including Data Lake and Lakehouse principles, medallion architecture, and data contracts
• Refactor, optimize, and modernize 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, scalability, and reliability across all platforms and pipelines
• Design and deliver self-service tooling and microservices that simplify platform usage
• Collaborate with Product, Machine Learning, and Data Science teams
• Contribute to architecture decisions and technical R&D initiatives

• 5+ years of professional experience in Data Engineering
• Strong Python and SQL development skills
• Hands-on experience with Apache Spark and PySpark, including query optimization and performance tuning
• Experience working with Databricks or Snowflake
• Practical experience with at least one major cloud provider such as Azure, AWS, or GCP
• Experience with stream processing technologies including Kafka or Spark Structured Streaming
• Strong understanding of ETL/ELT patterns, data modelling, and data warehousing concepts
• Experience with orchestration tools such as Apache Airflow or Azure Data Factory
• Knowledge of Infrastructure as Code tools including Terraform
• Understanding of production-grade systems, including observability, scalability, reliability, and performance
• Ability to independently lead technical initiatives from concept to delivery
• Strong communication and collaboration skills
• Upper-Intermediate or higher English level
• Familiarity with RAG pipeline design and LLM integration patterns
• Knowledge of data governance frameworks and tools such as Unity Catalog or Apache Atlas
• Experience with dbt for data transformation and modelling
• Familiarity with MLflow, Feature Stores, or ML platform integrations

• Remote work option
• Opportunity to work on high-impact, greenfield data initiatives
• Exposure to modern cloud-native data platforms and AI-ready data infrastructure
• R&D opportunities involving agentic AI architectures, event-driven systems, and LLM-ready data pipelines
• Cross-functional collaboration with Machine Learning, Data Science, and Product teams
• Knowledge-sharing and team-oriented environment