JOBWAVE VERIFIED

Senior Software Engineer — Ford Pro Data Analytics & AI

Company: Ford Motor Company
Role Title: Software Engineer / Senior Software Engineer
Portfolio: Pro 360 Data Analytics & AI
Business Domain: Ford Pro Commercial / Cloud-Native Services / Data Engineering
Location: Chennai, Tamil Nadu, India
Work Mode: On-site
Employment Type: Not specified
Experience Required: 8+ years in software engineering
Cloud Experience: 3+ years with GCP, Cloud Run, Spring Boot, and Java
Education: Bachelor's degree in Computer Science or Engineering
Salary: Not specified
Job ID: Not specified
Posting Status: Trending

Core Technology Stack

• Backend: Java, Spring Boot, Python
• Cloud Platform: Google Cloud Platform (GCP)
• GCP Services: Cloud Run, Cloud Spanner, Cloud Pub/Sub, GKE, Cloud Functions, Cloud Scheduler
• API Development: REST APIs, microservices, Swagger/OpenAPI documentation, Apigee, Azure API Management (APIM)
• DevOps and CI/CD: Tekton, Jenkins, GitHub, Cloud Build
• Code Quality and Security: SonarQube, Checkmarx, FOSSA
• Infrastructure as Code: Terraform
• Observability: GCP Logging, monitoring, alerting, SLOs, SLIs
• Engineering Practices: TDD, CI/CD, refactoring, clean code, pair programming, code reviews, Agile/Scrum, Extreme Programming
• Performance Engineering: API load testing, performance benchmarks, capacity planning
• Data Engineering: ETL, multi-tier data models, data quality monitoring, intelligence layers

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JOBWAVE INSIGHTS

1. Who Is This Role For?

This position is suited to a senior backend or cloud software engineer with strong Java/Spring Boot experience and hands-on GCP production experience.

The role combines cloud-native API development, microservices architecture, DevOps, performance engineering, and data platform capabilities. The engineer is expected to contribute to technical design, improve system reliability, lead engineering best practices, and deliver production-ready software for Ford Pro's commercial business.

It is not simply a Java developer role. The stated requirements emphasize production-scale APIs, cloud deployment, automated testing, observability, and performance.

2. Key Skills

Skill	Priority	What You Need to Demonstrate
Java	Critical	Production-grade backend development
Spring Boot	Critical	API and microservice development
Google Cloud Platform	Critical	Production cloud application delivery
Cloud Run	Critical	Deployment and operation of cloud-native services
8+ Years' Experience	Mandatory	Overall software engineering experience
API and Microservices Design	Critical	Scalable, secure, maintainable services
TDD and CI/CD	Critical	Automated testing and frequent delivery
Apigee / APIM	High	API publishing, documentation, and management
GitHub and Jenkins	High	Version control and delivery automation
SonarQube and Checkmarx	High	Code quality and security checks
Observability and SLOs/SLIs	High	Reliability and service performance
API Load and Performance Testing	High	Capacity analysis and bottleneck detection
Cloud Spanner and Pub/Sub	Strong advantage	GCP data storage and messaging
Python	Preferred	Scripting, data engineering, and supporting services
Tekton and Cloud Build	Preferred	Cloud-native CI/CD pipelines
Terraform	Preferred	Infrastructure as Code
GKE and Cloud Functions	Preferred	Additional GCP service experience

3. Priority Skills

Highest priority

• Java and Spring Boot
• GCP and Cloud Run
• 8+ years of software engineering experience
• Large-scale API development and cloud deployment
• Test-Driven Development and CI/CD
• API documentation and API management
• DevOps tooling and secure coding practices

Strong advantages

• Cloud Spanner and Pub/Sub
• Apigee and Azure API Management
• SLOs, SLIs, monitoring, and alerting
• API load testing and performance engineering
• Python and data engineering
• Terraform, Tekton, GKE, and Cloud Build
• Experience with global products and distributed systems

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JOB DETAILS

About the Role

The position supports Ford Pro's Pro 360 Data Analytics & AI portfolio, which provides operational data capabilities for Ford Pro technology products.

The broader portfolio includes ETL processes, multi-tier data models, data quality strategy and monitoring, third-party data evaluation, and intelligence layers that make data easier to use.

As a Senior Software Engineer, you will develop and evolve cloud-native services supporting analytical software tools and business processes.

Key Responsibilities

1. Java Backend and API Development

• Develop APIs and microservices using Java and Spring Boot.
• Build services on GCP, particularly Cloud Run.
• Work with services such as Cloud Spanner and Cloud Pub/Sub.
• Design maintainable, scalable, and secure backend components.
• Publish APIs with appropriate Swagger documentation and API management.

2. Cloud-Native Engineering

• Develop and operate cloud-native services.
• Support reliable deployments and production operations.
• Apply appropriate cloud services to meet application requirements.
• Contribute to reusable frameworks and components.

3. Engineering Excellence

• Participate in code reviews and architectural decisions.
• Promote clean code, refactoring, and reusable design patterns.
• Apply TDD and automated testing.
• Use pairing or mob programming as part of collaborative engineering practices.
• Improve development efficiency and reduce delivery risks.

4. Observability and Reliability

• Implement monitoring, logging, and alerting frameworks.
• Define and monitor service-level indicators (SLIs) and objectives (SLOs).
• Investigate reliability and availability issues.
• Improve production observability and operational readiness.

5. Performance Engineering

• Identify performance bottlenecks proactively.
• Establish performance baselines and benchmarks.
• Conduct API load and performance testing.
• Support capacity planning and performance improvements.

6. CI/CD and DevOps

• Use tools such as Tekton, Jenkins, GitHub, SonarQube, Checkmarx, and FOSSA.
• Improve automated build, test, security, and deployment pipelines.
• Support frequent, reliable production releases.
• Use cloud deployment tools and infrastructure automation where applicable.

7. Agile Product Delivery

• Collaborate with product managers and engineering team members.
• Translate business needs into incremental production-ready user stories.
• Participate in Scrum or Extreme Programming practices.
• Contribute to technical planning, delivery, and troubleshooting.

8. Data Engineering Collaboration

• Contribute to systems supporting ETL and data model capabilities.
• Help maintain data quality and reliable data delivery.
• Work with the broader Pro 360 analytics and AI portfolio.

The description contains an incomplete responsibility beginning with “Serve as a”; its intended meaning cannot be determined from the supplied text.

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REQUIREMENTS

Mandatory Qualifications

• Bachelor's degree in Computer Science or Engineering.
• At least 8 years of software engineering experience.
• At least 3 years of experience with GCP, Cloud Run, Spring Boot, and Java.
• Experience with Agile methodologies, including Scrum and Extreme Programming, in an anchor role.
• Experience launching large-scale global APIs on cloud platforms such as GCP or Azure.
• Practical experience with TDD, CI/CD, refactoring, clean code, and pairing or mob programming.
• Experience publishing APIs with Swagger documentation in Apigee and APIM.
• Experience with DevOps tools such as Jenkins, SonarQube, Checkmarx, and GitHub.

Important screening note: The wording places the 3+ years requirement against GCP, Cloud Run, Spring Boot, and Java. Confirm whether the employer expects three years with each technology individually or three years across the combined stack.

Preferred Skills

• Python and GCP or comparable cloud platforms.
• Cloud-native tools such as Cloud Run, GKE, Pub/Sub, Cloud Functions, and Cloud Scheduler.
• Apigee, GCP Logging, Terraform, Cloud Build, and Tekton.
• Proactive learning and problem-solving.
• Strong written, verbal, and interpersonal communication.
• Ability to work in a dynamic environment with ambiguous requirements.

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POTENTIAL GAPS TO CHECK

Before applying, assess your experience against these areas:

1. GCP production experience: Have you deployed and operated real applications on GCP, especially Cloud Run?
2. Java and Spring Boot: Can you demonstrate substantial experience designing and maintaining production APIs and microservices?
3. Large-scale APIs: Have you launched or operated APIs serving multiple systems, teams, or global users?
4. API management: Do you have practical experience with Apigee and Azure API Management, including publishing and documenting APIs?
5. Testing discipline: Have you used TDD, automated testing, and CI/CD in day-to-day development?
6. DevOps tools: Can you demonstrate hands-on use of Jenkins, GitHub, SonarQube, and Checkmarx?
7. Reliability engineering: Have you defined or worked with SLOs, SLIs, monitoring, and alerting?
8. Performance engineering: Have you performed load testing, established performance baselines, or resolved production bottlenecks?
9. GCP ecosystem: Have you worked with Cloud Spanner, Pub/Sub, GKE, or Cloud Functions?
10. Seniority: Can you show technical ownership and influence over engineering practices consistent with an 8+ year profile?

If your background is mainly Java development without GCP production experience, the cloud requirements may be a significant gap. Likewise, general cloud exposure alone may not satisfy the specific Cloud Run and API-management requirements.

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INTERVIEW PREPARATION

Phase 1 — Java and Spring Boot

Prepare for:

• Java collections, generics, streams, and exception handling
• Concurrency and thread safety
• JVM memory management and garbage collection
• Spring Boot dependency injection and auto-configuration
• REST controllers, validation, and exception handling
• Spring Security and authentication/authorization
• Database transactions and persistence
• Unit testing and integration testing
• Microservice design and resilience patterns

Phase 2 — GCP and Cloud-Native Architecture

Focus on:

• Cloud Run deployment, scaling, concurrency, and configuration
• Cloud Spanner data modeling and transaction concepts
• Pub/Sub topics, subscriptions, retries, and message processing
• GKE versus Cloud Run use cases
• Cloud Functions and Cloud Scheduler
• Service accounts, IAM, and least-privilege access
• Secret management and secure configuration
• Cloud Logging, metrics, and alerting
• Designing reliable and cost-conscious cloud applications

Be prepared to explain why you would choose one GCP service over another for a particular workload.

Phase 3 — API Design and DevOps

Prepare for:

• REST API design and versioning
• Swagger/OpenAPI documentation
• API gateways, authentication, quotas, and rate limiting
• Apigee and Azure API Management
• Jenkins and Tekton pipeline design
• CI/CD stages, quality gates, and rollback strategies
• SonarQube static analysis
• Checkmarx security scanning and software composition analysis
• FOSSA and open-source license or dependency compliance
• Terraform fundamentals and infrastructure automation

Phase 4 — Reliability and Performance Engineering

Review:

• Service-level objectives and indicators
• Availability, latency, throughput, and error rates
• Distributed tracing, logs, and metrics
• Load testing and stress testing
• Capacity planning and bottleneck analysis
• Caching, connection pooling, and database optimization
• Retry policies, timeouts, circuit breakers, and idempotency
• Pub/Sub failure handling and duplicate message processing
• Production incident analysis and root-cause investigation
• TDD, refactoring, and engineering quality

Final Interview Focus

Prepare one end-to-end architecture example that demonstrates:

1. A business requirement and API design.
2. Java/Spring Boot service implementation.
3. Deployment on GCP Cloud Run.
4. Integration with storage and messaging services.
5. Authentication and security controls.
6. Automated tests and CI/CD.
7. Observability through logs, metrics, alerts, and SLOs.
8. Load testing and performance improvements.
9. Production support, trade-offs, and lessons learned.

At this seniority, be ready to explain architectural decisions, alternatives considered, reliability trade-offs, and measurable outcomes—not just individual technology features.

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PORTFOLIO PROJECT RECOMMENDATION

Cloud-Native Commercial Analytics API

Build a demonstration service that exposes commercial sales analytics through production-style APIs.

Suggested architecture

• Java and Spring Boot for REST APIs.
• GCP Cloud Run for deployment.
• Cloud Spanner or a suitable development database for persistence.
• Pub/Sub for asynchronous event processing.
• Swagger/OpenAPI for API documentation.
• GitHub and Jenkins or Tekton for CI/CD.
• Terraform for infrastructure provisioning.
• Automated unit and integration tests.
• Logging, metrics, alerting, and load testing.

Suggested features

• Retrieve sales summaries and reporting periods.
• Filter results by region, dealer, or product category.
• Process sales updates asynchronously.
• Validate incoming records and handle duplicate events.
• Add authentication, error handling, and API versioning.
• Track request latency, error rates, and service availability.

Why this project helps: It demonstrates the combination of backend development, GCP deployment, API design, automation, and reliability practices emphasized by this role. Clearly label any simulated production metrics as test results rather than real customer traffic.

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FINAL RECOMMENDATION

Overall fit: Strong for a senior Java/Spring Boot engineer with substantial GCP production experience and a mature DevOps mindset.

This role is particularly suitable for candidates who have delivered large-scale APIs, worked with Cloud Run, implemented automated testing and deployment, and improved production reliability.

Before applying, prioritize verification of three requirements:

1. Eight or more years of software engineering experience.
2. Three or more years of relevant Java, Spring Boot, GCP, and Cloud Run experience.
3. Hands-on API management experience with Apigee and APIM, alongside the listed DevOps tools.

The combination of cloud-native backend engineering, data platform capabilities, and performance/reliability ownership makes this a senior engineering position rather than a conventional application-development role.