Requirements:

- Bachelor's or Master's degree in Computer Science, Engineering or a related field

- 8+ years of experience in Data Engineering with strong big data expertise

- Deep expertise in Databricks (2+ years of hands-on experience), Spark and SQL (including Spark SQL)

- Strong data modeling and ETL experience with large-scale datasets

- Hands-on experience orchestrating data pipelines with Airflow and CI/CD (GitHub Actions or similar)

- Proven experience with Delta Live Tables, Unity Catalog, Delta Sharing and SQL Warehouse (server and serverless)

- Proficiency in Python for data engineering workflows

- Familiarity with analytics engineering and dbt

- Experience with data governance, compliance and security in a cloud environment

- Experience adopting software engineering best practices (testing, modularization, refactoring, automation)

- Ability to optimize data systems for performance and cost efficiency in cloud environments

- Exposure to AI/ML frameworks for development efficiency

- Demonstrated ability to lead projects independently and mentor junior engineers

- English: Upper-Intermediate or higher

Nice to have:

- Experience with serverless architectures

- Understanding of pricing / financial data domains

- Experience with cascading formula-based financial calculations

- Familiarity with enterprise client support workflows

Soft Skills:

- Strong self-management: ability to work independently and meet deadlines

- Proactive and ownership-driven mindset

- Effective and clear communication, including in distributed teams

- Collaborative, open to feedback, and focused on shared goals

- Strong problem-solving skills, especially in ambiguous contexts

- High attention to detail and code quality

- Adaptability to shifting priorities and new technologies

Responsibilities:

- Serverless migration: adapt existing pipelines / code to run on different cluster types (serverless) without issues

- CI/CD ownership: maintain and improve CI/CD processes, propose architectural improvements

- Pipeline monitoring: proactively check repositories, review merge requests and team members' changes

- Functional and technical work: write pipeline code and understand the business logic (pricing domain)