DATA ENGINEER (Data Science Enablement)

Job title:

Company:

Raiffeisen Bank

Job description

What will your tasks be?

Participate in the lifecycle of data science projects, incl. design and development of data processing and monitoring pipelines

Work with the state-of-the-art cloud infrastructure (AWS, Databricks) * Assemble large, complex data sets to meet functional / non-functional business requirements

Develop, maintain and optimize ELT and ETL pipelines (incl. incidents investigation, writing “postmortems”)
Continuously support internal consumers (data analysts, data scientists) in best data engineering practices and automation of development pipelines
Find and adopt best practices, share your knowledge with a team and constantly learn from others

Why is it worth working with us?

You’ll work both locally and in an international analytics team at a leading bank
Be part of a huge international community of Data Team sharing learnings, best practices, use cases regularly
Data science courses provided by the group
Flexible Home Office opportunity (up to 80%)
You can choose from 16 types of Cafeteria benefits: SZÉP card, extra day off, commuting support, health insurance, tickets to cultural events and sports events,
We provide housing loan support, an employee account package
A modern working environment, dining rooms and cafes await you
As part of a well-being program, we pay attention to the physical and mental health of our colleagues

Application requires

Structured and conceptual mindset coupled with strong quantitative and analytical problem-solving attitude.
Professional experience in designing and developing production ready data application and pipelines in cloud ecosystem.
Software engineering excellence: understanding of SDLC, Unit / Integration tests, Data Lake architecture.
Knowledge in PySpark and SQL (DDL, analytical functions, sub-queries, optimization of performance, principles for optimization).
Experience working within agile (scrum) methodology.
Fluent English, spoken and written.

Will be a plus:

BSc in Computer Science, Informatics, Software Engineering or related major
Solid knowledge of ML principles and frameworks, analytical libraries (e.g., pandas, NumPy, scikit-learn etc.)
Knowledge of DBT for ETL pipelines
Good comprehension of data warehousing principles, MDM, data models (LDM/PDM)
Experience with other programming languages beyond Python
Experience in developing CI/CD/CT pipelines (we’re using GitHub actions)