ING Bank Personeel
Senior Data Platform Engineer
Senior engineer who designs, builds, and operates ING's VISTA Data Analytics Fallback Platform — resilient lakehouse infrastructure (Spark, Airflow, Iceberg, Trino, object storage on Kubernetes/GCP) that keeps ING's critical analytics and reporting workloads running during disaster-recovery and fallback scenarios, including on-call and operational readiness work.
- Compensation
- PLN 9.6K–PLN 28K / mo
- Location
- Warszawa, Poland
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 4+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 4+ years of experience in Data Engineering, Platform Engineering, Infrastructure Engineering, or Site Reliability Engineering
- Hands-on experience in at least one domain, e.g. Data Processing & Analytics: Apache Spark, DBT, Python, SQL, BigQuery, Trino
- Data Lake & Storage: Apache Iceberg, Hive Metastore, MinIO/S3-compatible Object Storage, Google Cloud Storage
- Platform & Infrastructure: Kubernetes, Docker, Linux, Keycloak, Ceph, Ingress/Networking
- Reliability & Operations: Monitoring, Logging, Alerting, Data Replication, Backup & Restore, Disaster Recovery
- Experience building, operating, or supporting modern data platforms and distributed data processing environments
- Practical experience with Google Cloud Platform (GCP) and cloud-native technologies
- Experience with automation, CI/CD, and infrastructure-as-code practices
Nice to have
- Airflow-based orchestration platforms
- Keycloak, Ceph, or enterprise-scale platform services
- Designing and operating large-scale data platforms
- Disaster recovery, business continuity, or fallback solutions
- Modern lakehouse architectures based on Apache Iceberg
- Data replication and synchronization at scale
- Migrating workloads between cloud and on-premise environments
Full description
Expected salary
- senior specialist position: 9600 - 22 000 PLN
- expert position: 13 000 - 28 000 PLN
The financial ranges specified in the announcement are adjusted and may differ from the range specified in the remuneration regulations.
We are looking for you, if you:
- have 4+ years of experience in Data Engineering, Platform Engineering, Infrastructure Engineering, or Site Reliability Engineering
- have hands-on experience in at least one of the following domains and practical knowledge of related technologies: Data Processing & Analytics: Apache Spark, DBT, Python, SQL, BigQuery, Trino
- Data Lake & Storage: Apache Iceberg, Hive Metastore, MinIO/S3-compatible Object Storage, Google Cloud Storage
- Platform & Infrastructure: Kubernetes, Docker, Linux, Keycloak, Ceph, Ingress / Networking
- Reliability & Operations: Monitoring, Logging, Alerting, Data Replication, Backup & Restore, Disaster Recovery
- have experience building, operating, or supporting modern data platforms and distributed data processing environments
- have practical experience with Google Cloud Platform (GCP) and cloud-native technologies
- have experience with automation, CI/CD, and infrastructure-as-code practices
You'll get extra points for experience with:
- Airflow-based orchestration platforms
- Keycloak, Ceph, or enterprise-scale platform services
- designing and operating large-scale data platforms
- disaster recovery, business continuity, or fallback solutions
- modern lakehouse architectures based on Apache Iceberg
- data replication and synchronization at scale
- migrating workloads between cloud and on-premise environments
Your responsibilities will relate to:
- designing, building, and maintaining ING's Data Analytics Fallback Platform
- creating resilient data platform capabilities that support critical business workloads during disaster recovery and fallback scenarios
- developing and operating solutions based on Apache Spark, Apache Airflow, Apache Iceberg, Trino, and object storage technologies
- building and maintaining data processing, orchestration, storage, and metadata-management capabilities
- implementing replication, synchronization, reconciliation, and recovery mechanisms for critical datasets
- building and optimizing analytical and batch-processing pipelines using modern cloud and data technologies
- improving platform observability through monitoring, logging, alerting, and operational readiness practices
- ensuring platform reliability, security, recoverability, and operational resilience
- implementing backup, restore, disaster recovery, and business continuity capabilities