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

• Employees can work remotely