Engineering Manager - DevOps

Job Description :

The Engineering Manager is responsible for managing the performance and results of the multiple agile teams across the Engineering organization. This position will oversee the planning, staffing, budgeting, and management of expense priorities. They are also responsible for recommending and implementing changes to methods.

Responsibilities :

- Manages and is accountable for the performance and results of multiple agile teams.

- Manages the planning, staffing, budgeting, managing expense priorities, and recommending and implementing changes to methods.

- Provides leadership to agile teams and builds and leads a culture of accountability to meet business objectives and goals.

- Leads and supervises team(s) and assumes people management responsibilities including, but not limited to, the management of direct reports, and applicable subordinate team members, setting performance objectives, and conducting annual performance reviews.

- Develops staff members through coaching and fostering a positive work environment and mentors, and grows team leaders.

- Leads and manages the technical activities of multiple complex software development projects.

- Motivates teams to solve problems and deliver high-quality results.

- Establishes operational objectives and plans and delegates assignments to staff members.

Requirements :

- Strong Engineering Background.

- 10-20 years' experience leading diverse global teams.

- Ability to multitask and work on several projects simultaneously.

- Deep knowledge of Agile Scrum.

- Strong leadership skills.

- Strong verbal and written communication skills.

- Ability to drive third-party relationships when required. Exceptional problem-solving and

analytical skills.

- Human relations skills or combined skills in understanding, developing, and/or motivating

people.

- Strong grasp of CI/CD pipelines, automation, infrastructure as code (IaC), monitoring, and logging.

- Microservices Architecture : Understanding its benefits, challenges, patterns (e.g., API Gateway, Service Mesh), and when to apply it.

- Scalability & Resiliency : Knowledge of designing systems that can handle high loads and recover gracefully from failures.

- System Design : Ability to understand and contribute to high-level system designs, considering trade-offs (performance, cost, security, maintainability).

- Cloud Native Principles : Understanding how to build applications that leverage cloud services effectively.

- Cloud Agnostic Knowledge (preferred) : Familiarity with core concepts across major providers (AWS, Azure, GCP).

- Specific Cloud Provider (at least one deep) : Understanding the services, pricing models, security features, and best practices of at least one major cloud platform.

- Containerization & Orchestration : Strong understanding of Docker and Kubernetes.

Serverless Computing : Awareness of serverless functions and their use cases.

- Data Architecture Fundamentals : Understanding how data flows, is stored, and accessed in complex systems (e.g., data lakes, data warehouses, streaming data).

- Big Data Technologies (conceptual) : Awareness of technologies like Spark, Hadoop, and their applicability.

- AI/ML Understanding (conceptual) : Knowing the basics of machine learning pipelines, common algorithms, and how AI/ML can drive business value, without necessarily building

models themselves.