Für unseren Kunden suchen wir ab sofort einen Cloud Native Data Architect (m/w/d) Data Lake, Kafka & CI/CD für den voraussichtlichen Zeitraum bis 07/2025 mit der Option auf langfristige Verlängerung. In diesem großen Greenfield-Projekt kann unter Nutzung moderner Technologien ein Beitrag zur Energiewende geleistet werden. Das Projekt ist als Vollzeitbeschäftigung geplant. Mindestens eine Woche pro Monat findet vor Ort in Frankfurt am Main statt.

Hinweis: Bitte sehen Sie von Bewerbungen ab, die nicht alle Mindestkriterien erfüllen.

Project Description:
The team is building an internal platform for software product developers to accelerate the development and delivery of software products to tackle the massive challenges facing the energy sector. The Platform is a service oriented, cloud-native platform that is being built to provide application teams with self-service capabilities to develop, run and operate their software products. The Platform provides services for application infrastructure, data, service lifecycle management, application build and delivery as well as services to operate their software products. The platform is deployed as a hybrid cloud, encompassing both private cloud and selected public clouds.

Targets in that Position:
- Conceptualization and designing of a robust reference architecture tailored for cloud-native applications
- Conducting workshops with the project tenants to understand and modernize the application architecture for a successful adoption of the platform
- Consulting project tenants who are building data-intensive applications with known good practices for data architecture in a cloud native environment
- Enabling project tenants on how to de-centralize and model the data within a microservices architecture analyzing from a business domain perspective (eg: database per service pattern, transient and persistent data within bounded contexts)
- Coordination with project tenants on choosing suitable data storage solutions based on business needs. (eg: Relational DBs, NoSQL, Time series DB, delta-lake, caches, etc.)
- Consulting project tenants on how to effectively share data between application services at scale (eg: messaging with Kafka)
- Performing thorough initial analysis of project requirements and deliverables
- Conducting regular reviews and audits to maintain high standards and continuous improvement
- Engaging with the project’s Product Management for alignment of the architectural designs with product goals
- Coordination with Customer Success and Architecture to ensure cohesive incorporation of their perspectives into the reference architecture
- Coordination and alignment with Product Management for identifying and prioritizing of features and user stories based on project and user needs

Must-Have:
- Deep understanding of data architectures (incl. data quality and data SLOs) in the context of cloud-native and distributed environments and the ability to articulate data concepts to different audiences
- Proven experience in helping development teams navigate the CAP theorem with trade-offs when choosing data storage technologies
- Hands-on experience with the following:
o Relational Databases (eg: PostgreSQL)
o NoSQL Databases (eg: MongoDB, Cassandra)
o Time series Databases (eg: TimescaleDB, InfluxDB)
o Graph Databases (eg: Neo4j)
o Data Warehouse and Data Lake architectures
o Messaging/Data Streaming Systems (Apache Kafka)
o Data processing and workflows (eg: Spark, Dagster, Apache Airflow, etc)
o the Kubernetes ecosystem to build, deploy and troubleshoot container workloads
o CI/CD pipelines and related tooling to automate code build, test and deployment
o Development in at least one programming language (eg: java, python, golang)
- Applying CI/CD principals to databases for rapid integration of database changes into application development
- Experience in building data solutions that enable data democracy in the organization and prevents data silos while ensuring data governance and compliance
- Strong knowledge of data security and the ability to specify at an architectural level the security best practices that must be incorporated when working with Data (encryption, access control, data classification and compliance, etc)
- Demonstrated experience in explaining the maturity required to use cloud native data technologies – not only on the IT side but especially on the Business side of an enterprise

Nice-to-Have:
- Relevant certifications in cloud and cloud native technologies
- Understanding about the principles of Data Mesh & data products

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