Targets in that Position
Data core services architecture:
– Design and prototype scalable data architectures, including databases, data lakes, and data warehouses as
managed services in the private cloud (ie K8s based).
– Design and prototype functionalities for these managed services, such as backup/restore, IAM, observability
integration etc. and update/upgrade schemes
– Design and specify managed services adhering to product specification, as well as SLAs
– Design and prototype Kubernetes-based deployment strategies for scalable, highly reliable, and manageable
data technologies.
– Evaluate different Data technologies options for finding the best basis for managed services
– Design and prototype cross-cutting aspects across different Data services in a consistent and coherent way
– Collaborate with Services, DevOps and Infrastructure teams to optimize data technology deployment processes
within a Kubernetes environment.
– Document best practices for knowledge sharing and future reference
– Work closely with engineering team to ensure proper implementation of defined architecture, alignment on tech
stack decisions, compliance with architecture standards

Skill Requirements
Must-have competencies / Must-have skills
Proven hands-on software development experience
Proficiency in data processing languages ​​such as SQL, Java, Python or Scala
Deep K8s skills and experience
Knowledge and experience with the Data technologies/frameworks:
– RDBMS ( PostgreSQL/MySql etc.)
– NoSQL Storages (MongoDB, Cassandra, Neo4j etc.)
– Timeseries (InfluxDB, OpenTSDB, TimescaleDB, Prometheus etc.)
– Workflow orchestration (AirFlow/Oozie etc.)
– Data integration/Ingestion (Flume etc).
– Messaging/Data Streaming (Kafka/RabbitMQ etc.)
– Data Processing (Spark, Flink etc.)
And/Or with their Cloud provided counterparts, ie, Cloud Data/Analytics services (GCP, Azure, AWS)
Knowedge of Data technologies not only just from usage but also from deployment prospective and experience
with on-prem deployments
Knowledge and experience with reference Big Data architectures (Warehouse, Data Lake, Data Lakehouse) and
their implementation.
Experience in implementing and operating data intensive applications
Strong focus on DataOps/DevOps