Data Engineer

A Data Engineer is responsible for providing solutions for data engineering design, including high-level design (HLD) review and low-level design (LLD) build-out. They will collect, process, and analyze large datasets. Advanced SQL experience, including query writing, optimization, data aggregation, and stored procedures, as well as experience writing data pipelines using Python and PySpark, is essential. Additionally, they will effectively manage and optimize data processes, contributing to the overall success of projects.

Must-have requirements:

• Strong SQL experience – ability to write, optimize, and maintain SQL queries, stored procedures, and data aggregations.

• Experience in writing data pipelines using Python and PySpark.

• Familiarity with Azure cloud technologies – experience with Azure Data Lake Storage (ADLS Gen2), Synapse, or ADF.

• Data modeling experience – ability to design efficient relational data schemas.

• Ability to work in a Scrum environment – active participation in Agile team processes.

Nice-to-have qualifications:

• Understanding of DevOps and CI/CD concepts.

• Experience working with big data solutions and large-scale data processing.

• Familiarity with different ETL/ELT approaches and best practices.

• Opportunity to be part of a global organization;

• Encouraging working atmosphere, engaging activities, and extra benefits (e.g. additional medical insurance (covers gym subscription), team events, outreach activities, and others);

• Hybrid work model with remote work opportunities;

• Open, modern, and ergonomic work environment when working from the office with the multicultural community;

• Opportunity to grow professionally and personally (e.g. Udemy) and continuously develop within the domain.