Applicant Architect

Lead Engineer variable marketing incentive systems is a hands-on design/development role that own the architecture, technical design, realization for a complex micro services ecosystem and build a greenfield open stack platform for a large-scale technical modernization

Modernize the technology stack for variable marketing incentive systems based on MACH architecture principles.

Model the use of modern software engineering practices (i.e.TDD, BDD, CI/CD, Shift left, 12 factor applications etc.), API design, and architecture to support integration with existing Ford software products as well as external cloud-based services.

Collaborate with Fords Enterprise Architecture organization to rationalize technologies, further leverage current technology offerings already in use, and identify gaps/opportunities.

Overall responsibility and accountability for API design and backend Spring boot microservices suite on Google cloud platform

Responsible for overall design and evolution of one or mode modules (Microservices) in one of the variable marketing incentive products. Ideal candidates will research the existing application footprint and recommend solutions to run application workloads in futuristic Architecture landscape

Modernize legacy systems to modern stack and significantly improve Fords current capabilities and ensure these platforms can grow to meet increasing demands

Design, build POCs on latest cutting-edge technologies as well as contribute to constructing, deploying highly scalable and robust cloud based intelligent solutions.

Contribute to Fords Product Driven Organization (PDO) model by identifying improvements and areas that help to reduce dependencies and increase autonomy for teams to delivery.

Strong hands-on experience of 12+ years in Java/J2EE/Spring framework/Spring boot

Experience with the following: Microservices architectures (using Spring boot/Micronaut/Ratpack/Quarkus), Cloud-Native architectures, Event-driven architectures, APIs, Domain-Driven Design, Public Cloud (Google Cloud), Serverless, Kubernetes, Docker, DevOps, building scalable, reliable, available solutions, and/or performance testing.

Good experience in api design adhering to the MACH architecture.

Conversant in multiple programming languages Java, Python.

Thorough knowledge of multi-threading, concurrency, and parallel processing concepts including scalability, performance, and data consistency

Good expertise in REST, Messaging (KAFKA, RABBITMQ, cloud pub/sub etc.), stream processing (SPARK, STORM etc.), NoSQL as well as database Systems (RDBMS, NO SQL Stores like Cassandra, HBase, Mongo, Memcached etc

Good experience in event driven architecture

Experience in Cloud Native systems, Transactional Systems, Multi-Tenancy, five-nines availability and Containerization technologies

Experience in collaborating and partnering with other technical domain experts such as cloud, security, SRE, and DevOps.

Experience in building Structured, Semi-Structured and Unstructured data stores with a good understanding of RDBMS, No-SQL databases and strong exposure to data modelling, data access patterns, data replication, active-active polyglot persistence setup.

Experience in data migration from legacy RDBMS to modern RDBMS/NoSQL

Hands-on skills in application performance management coming up with performance metrics, ability to use to observability tools to identify api / job performance issues, remedial measures to improve performance

Excellent communication and interpersonal skills

Ability to work effectively in a remote/virtual work setting with other global team members

Proven facilitation skills - able to effectively drive discussion among diverse perspectives and reach a decision or recommendation

Effectively work with cross-functional teams across the organization inside and outside of the technology and software organization

Work with business teams, understand the product features and come out with viable solution options both in terms of cost and timelines

Work with delivery managers/leaders on incremental implementation approaches like strangler fig pattern

Work closely with delivery leaders and upfront highlight any major risks and remediation for the same