Data Engineer

The Team

Within ING, there is ING Analytics (INGA) - a major driving force in its digital transformation of the bank.

By creating measurable value for ING and its customers through world-class analytics products and services, INGA helps ING to become a leader in data-driven decision-making. Among others, INGA delivers call & speech analytics, generative AI in risk summarization, insights into portfolio performance, advanced ESG data insights (commercial purposes), data analytics platforms (cloud and on-premises), and many more.

Roles and Responsibilities

We are looking for a Data Engineer to join us in Istanbul. In this role, you will be responsible for designing, building, and optimizing complex data pipelines to process transactional and other data. You will collaborate closely with data scientists and analysts in developing and maintaining data solutions and INGA’s data architecture to support the next generation of products and data initiatives.

We are looking for curious and empathetic individuals who are committed to developing themselves and learning new skills. We hire people based on an evaluation of their potential and support them throughout their time in Istanbul. Come join us!

A working day in the life of a Data Engineer

A typical day for a data engineer starts with coffee, catching up with colleagues, and drafting a plan for the coming day. After the standup, you will start exploring the data to solve business problems, building and maintaining the production-ready infrastructure, and building the data pipelines that clean, transform, and aggregate data from disparate sources. Being an advocate for best practices in data engineering is important while working on such tasks.

After lunch, you organized a knowledge-sharing session to explain data security, compliance, and privacy standards to the rest of the data engineering team. Finally, to wrap up the day, you collaborated with the data science and analysts' teams to develop data models and pipelines for research, reporting, and machine learning.

How to succeed

Minimum Qualifications

• Bachelor's degree or equivalent practical experience.
• Excellent communication skills, capable of working collaboratively in a semi-remote, globally distributed team.
• Hands-on experience building complex data pipelines (e.g., PySpark, Airflow).
• Comfortable with Python and SQL.
• Experience designing data models, and using SQL database management systems, along with data processing using traditional and distributed systems.
• Knowledge of software engineering best practices and tools (e.g., CI/CD tools, testing).

Preferred Qualifications

• Curious mindset; you are collaborative and passionate about helping others to grow and improve.
• Experience with deployment and provisioning automation tools.
• Experience with monitoring and observability (ELK stack).
• Knowledge of MLOps architecture and practices.
• Knowledge of data manipulation and transformation.