Senior Data engineer

Quick Facts

Senior Data Engineer role focused on modernizing and unifying a global data ecosystem by building a next-generation data platform leveraging Databricks on AWS, Medallion Architecture, and ClickHouse for low-latency analytics.

Description

You will play a key technical role within a dedicated squad, combining hands-on development with end-to-end delivery ownership. The work emphasizes production-grade data pipelines, driving data quality, establishing automated testing practices, and translating business requirements into scalable engineering solutions.

Responsibilities

• Design and build production-grade data pipelines and data platform components using Databricks on AWS, PySpark, Python, and SQL.
• Implement scalable data models based on the Medallion Architecture framework (Bronze, Silver, and Gold layers).
• Architect and optimize low-latency, real-time analytics data paths using ClickHouse.
• Drive engineering quality by building automated unit, integration, and data quality testing frameworks without relying on dedicated QA resources.
• Collaborate directly with client stakeholders and team leadership to refine backlog items, clarify requirements, and run technical discussions.
• Conduct design and code reviews to enforce system reliability, maintainability, and architectural standards.
• Provide pragmatic support and maintenance for a legacy Hadoop/Kafka reporting platform (~10% effort split) while supporting the transition to the new platform.

Requirements

• 5+ years of commercial data engineering experience building, scaling, and maintaining enterprise data platforms.
• Strong hands-on experience with Databricks, Spark/PySpark, Python, and advanced SQL.
• Proven track record of designing data models and architectures using the Medallion Architecture pattern.
• Practical experience implementing high-volume, low-latency analytics solutions using ClickHouse.
• Solid engineering background in automated testing methodologies (unit, integration, and data quality validation) within data pipelines.
• Demonstrated capability to translate complex business needs into technical designs and clear backlog requirements.

Would be a plus

• Practical experience within the AWS cloud ecosystem.
• Familiarity with real-time streaming architectures and frameworks such as Apache Kafka.
• Domain knowledge in payment processing, financial services, or merchant reporting systems.
• Conceptual or hands-on exposure to MLOps frameworks or integrating AI/ML workflows into Databricks platforms.

Benefits

• Opportunity to work on bleeding-edge projects
• Work with a highly motivated and dedicated team
• Competitive salary
• Flexible schedule
• Benefits package (medical insurance, sports)
• Corporate social events
• Professional development opportunities
• Well-equipped office