Technical Anchor, Software Engineering

 
As a Technical Anchor, you will oversee the technical design, development, and delivery of business-critical applications and high-throughput data platforms. You will establish engineering excellence across full-stack systems, architect resilient Java-based microservices, deliver responsive front-end data exploration interfaces using React or Angular, and design enterprise-grade cloud data streaming and analytical pipelines on GCP. You will lead distributed engineering squads across local and global locations, driving cloud modernization, data governance, and operational reliability to fuel Ford’s digital transformation.

1. Technical Leadership, Architecture & Modernization

• Anchor Technical Delivery: Act as the primary technical anchor, translating complex business requirements from legacy applications in GDIA into scalable, fault-tolerant system designs, architectural blueprints, and execution roadmaps.
• Architecture Standards: Drive the evolution of Service-Oriented Architecture (SOA), cloud-native microservices, and event-driven patterns. Establish coding standards, API contracts (OpenAPI/Swagger), and architectural governance across squads.
• Legacy System Modernization: Lead legacy modernization initiatives by re-architecting monolithic services into modular, containerized workloads on GCP, engineering continuous ingestion flows from heterogeneous enterprise platforms.
• Engineering Mentorship & SDLC Excellence: Champion agile engineering best practices, enforce test-driven development (TDD), lead comprehensive code reviews, and mentor engineers across full-stack, data engineering, and cloud domains.

2. Full-Stack & Microservices Engineering

• Backend Services: Architect and develop robust, high-performance microservices and RESTful APIs using Java 17+, Spring Boot, Spring Cloud, and supporting synchronous transactions and asynchronous event-driven flows.
• Frontend & Data Visualization: Build responsive, user-centric interfaces using Angular, React, and Node.js that integrate seamlessly with backend APIs to deliver intuitive self-service data access, reporting, and interactive data visualization.
• API Gateway & Service Mesh: Design and standardize API gateway topologies using Apigee; enforce service discovery, circuit breakers, rate limiting, and resilient inter-service communication.
• Performance Engineering & Optimization: Profile and optimize distributed systems end-to-end, including JVM tuning, connection pooling, caching strategies, and database query tuning across relational (PostgreSQL, MySQL) and NoSQL datastores.

3. Cloud Data Engineering & Analytics

• Scalable Data Pipelines: Architect, build, and maintain real-time streaming and high-volume batch data pipelines leveraging GCP Dataflow (Apache Beam), Dataproc (Spark/Hadoop), and Cloud Pub/Sub.
• Modern Data Warehousing: Standardize, model, partition, and optimize enterprise datasets within Google BigQuery to ensure exceptional SQL query performance, cost efficiency, and real-time analytical capabilities.

4. Security, Governance & Compliance

• Zero Trust Security: Enforce least-privileged access control across applications, integrating Multi-Factor Authentication (MFA), ADFS, OAuth2/OIDC, and method-level access authorization.
• Cloud & Data Governance: Implement robust cloud security frameworks, including Role-Based Access Control (RBAC), data encryption (in transit and at rest), data masking, and GCP-native row-level and column-level security.
• Regulatory Compliance: Partner with cybersecurity, compliance, and governance teams to maintain adherence to enterprise data privacy mandates and corporate security standards.

5. DevOps, Cloud Infrastructure & Observability

• Infrastructure as Code (IaC): Automate and manage repeatable cloud infrastructure and platform provisioning using Terraform.
• CI/CD Automation: Design and optimize enterprise CI/CD pipelines using GitHub Actions, Tekton, and build automation tools (Maven, Gradle) for automated testing, static code analysis, and zero-downtime deployments.
• Operational Reliability & Telemetry: Establish proactive monitoring, tracing, and alerting frameworks across Google Kubernetes Engine (GKE), Cloud Run, and Cloud Functions to diagnose distributed system bottlenecks and ensure platform health.
• AI/ML Innovation: Apply foundational AI and Machine Learning (ML) concepts, including Generative AI and Large Language Models (LLMs), to accelerate engineering workflows and enhance software capabilities.

• Education: Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, Information Systems, or a related technical discipline (or equivalent practical experience).
• Professional Experience: 10+ years of progressive software engineering experience delivering enterprise-scale solutions across full-stack development, distributed computing, and data platforms.
  Core Technical Competencies:
• Backend Engineering: Deep expertise in Java (Java 17+), Spring Ecosystem (Spring Boot, Spring Cloud, Spring Data, Spring Security), and scripting proficiency.
• Frontend Engineering: Hands-on experience developing data visualization and exploration interfaces using React, Angular, or Node.js.
• GCP Cloud Platforms: Proven track record architecting and deploying systems on Google Cloud Platform (GCP), specifically BigQuery, Cloud Run, GKE and Cloud Pub/Sub.
• Database Systems: Strong practical expertise with relational databases (PostgreSQL, MySQL), NoSQL datastores, and columnar analytical warehouses.
• DevOps & IaC: Hands-on mastery of Infrastructure as Code using Terraform, containerization (Docker, Kubernetes), and automated CI/CD tools (GitHub Actions, Tekton, Maven/Gradle).
• API & Testing Standards: Deep knowledge of RESTful API design (OpenAPI/Swagger) paired with a test-driven mindset using JUnit and Mockito