Für unseren Kunden aus dem Energiesektor suchen wir ab Januar einen Data Engineering Plattform Service Lead (m/w/d). Die Tätigkeit erfolgt in der Regel Remote und ca. 1 mal pro Monat für ein paar Tage am Stück in Frankfurt oder Berlin.

Projekt:

Our client 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. 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 select public clouds.

The Product Line Data within the program project is responsible for developing data services of a hybrid data platform, such as a private cloud as well as public cloud and towards a future KRITIS infrastructure. Data will be providing managed SQL and NoSQL database services, as well as data processing and messaging capabilities and solutions.

Objectives:

Objective: Provide Project based Technical Leadership

- Setting the technical direction for implementation, taking responsibility for product quality and timely delivery of the increment.

- Ensuring adherence to best practices in coding, testing, deployment, and documentation

- Establishing and cultivating a culture of technical excellence, using standards and code reviews to increase delivery speed and ensure the quality of value reaching the customer.

- Taking project responsibility of technical coordination within the group of stakeholders and champions the removal of impediments, empowering the stakeholders to maintain a sustainable delivery flow.

- Providing guidance and mentoring for the stakeholders, fostering a culture of continuous learning to improve their collective technical skills.

Objective: Ensure consistency and feasibility of plans

- Coordination with the stakeholders, such as product owner, to create specific and achievable roadmap milestones.

- Evaluating the feasibility, risks, and technical implications of proposed goals and requirements.

- Contributing to the cycle planning with realistic effort estimation and identification of risks and dependencies.

- Driving agility through promoting agile practices and embracing of change by adapting quickly to shifting environment while maintaining delivery momentum.

- Proactively contributing insights during Backlog Refinement to help break down features, clarify implementation details, and provide estimates

- Alignment of work to be done and decisions with project team objectives, ensuring transparency and shared ownership.

Objective: Ensure Consistent and Measurable Value Delivery

- Ensuring priorities and efforts remain aligned with broader business goals and customer needs.

- Translating the product vision into actionable practice for the stakeholders, identifying technical risks and potential roadblocks early in the process.

- Coordination with stakeholders, such as Product Ownership, Architecture, Development, QA, and Operations to deliver cohesive, high-quality solutions.

- Providing continuously validation of that planned work delivers meaningful value and supports strategic outcomes.

- Monitoring of delivery progress and remove blockers to maintain predictable, efficient flow.

Objective: Build Data Service Solutions on the Kubernetes Platform

- Conceptualization of design and deploy containerized data services such as PostgreSQL, TimescaleDB, and Kafka, MongoDB or Spark on Kubernetes with declarative patterns using helm charts and GitOps.

- Driving the development and use of Kubernetes Operators or custom controllers to automate the full lifecycle (provisioning, scaling, backup, restore) of stateful data workloads.

- Give recommendations on the optimization to improve overall performance, reliability, and scalability of data services within the cluster.

- Implementation of observability for data workloads through logging, metrics, and alerting.

- Implementation of an observability stack (e.g., Prometheus, Grafana, Fluentd) to provide granular monitoring, distributed tracing, metrics, and alerting for data services.

- Development of Automated deployment and operational processes through CI/CD pipelines

Objective: Build and Maintain the Kubernetes Management Platform

- Designing, installation, configuration, and management the full lifecycle (upgrade, patch, capacity planning) of Kubernetes clusters for production and non-production environments using Infrastructure-as-Code (IaC) tools e.g. Terraform.

- Ensuring the platform setup supports high availability, scalability, and fault tolerance.

Providing of automation routine operations where feasible to support efficient lifecycle management.

- Implementation of Kubernetes-native networking policies and service meshes, and security policies, including Role-Based Access Control (RBAC), Pod Security Standards, network segmentation via Kubernetes-native network policies, and a centralized secrets management system.

- Deployment and management a Service Mesh to standardize traffic management, policy enforcement, and mutual TLS across microservices.

Configuration of role-based access control (RBAC), secrets management, and security contexts.

- Integration of logging and monitoring tools (e.g., Prometheus, Grafana, Fluentd) for system observability.

Must-have experience

- 5+ years of general IT experience, 5+ years of leadership experience, shown by successful implementation of large scale IT projects

- 3+ years of Data services experience

- Extensive experience in developing and maintaining SQL and NoSQL databases and streaming solutions

- Experience with agile methodologies, i.e. Scrum and Kanban

- Proven experience in Data Engineering with a focus on designing and implementing scalable data architectures.

- Familiarity with modern data technologies and cloud services., represented by project history

Must-have language skills:

English in speech and writing (at least C1)

Preferred experience:

- Deep expertise in Kubernetes and Kubernetes Operators

- Experience in Platform Engineering