We are looking for you, if you have:
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hands-on experience building complex data pipelines,
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experience with Apache Airflow, Spark, Python,
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experience in setting up and optimizing both SQL and noSQL data stores (MS-SQL, Hive), as well as familiarity with object storage services (e.g., S3),
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experience with deployment and provisioning automation tools (e.g., Docker, Kubernetes, CI/CD).
You'll get extra points for:
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experience working in cloud environment (e.g., Azure DevOps, GCP),
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extensive experience with Spark, Hadoop, SQL & relational databases, data models and ETL pipelines,
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experience in building GitLab CICD pipelines,
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experience in managing and further developing distributed systems and clusters for batch processing,
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knowledge of MLOps architecture and practices,
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experience with modern data modeling concepts and large scale data warehousing architecture.
Your responsibilities:
- work with the latest concepts and technologies in the field of Data Engineering,
- as an engineer, you will be responsible to design and build the data pipelines for one of many use cases catering various stakeholder requirements,
- your day-to-day activities can look like this: implementing new features in Kedro/PySpark, testing them in Apache Airflow, deploying it in our in-house platform running on Kubernetes, as well as optimizing the data in the permanent data store.
Information about team:
The Wholesale Banking Advanced Analytics team is a team of 100+ people that has the mission to make Wholesale Banking in ING data-driven. We do this by combining UX research and engineering with data science to deliver high-value solutions and products for our organization. We work in a fun and creative environment, and we’re dedicated to bringing out the best in both each other and our projects. We have offices in Amsterdam (main office), Warsaw, Katowice, Bucharest and Manila.
The role naming convention in the global ING job architecture will be “Engineer III”.
