Data Architect

As a Data Architect at Ford Motor Company, you will be responsible for designing and implementing data solutions that meet the company's needs. This includes architecting the migration of legacy data environments, developing ETL jobs and data pipelines, and closely collaborating with other professionals to inform data architecture decisions. You'll also build data solutions in the cloud and may work with various data storage technologies.

To be successful as a Data Architect at Ford Motor Company, you should have at least 10 years of experience with Python or Go, adept in REST APIs, as well as a strong background in architecting migrations of legacy data environments. Furthermore, experience mentoring junior developers and fluency in various data management technologies and practices are key qualifications for this role.

Candidates applying for the Data Architect role at Ford Motor Company should have proficiency in Python, SQL, and Go, as well as experience with cloud technologies like GCP. Familiarity with databases such as PostgreSQL, NoSQL, and Big Query, along with understanding CI/CD practices and DevOps tools, will also be beneficial for this position.

Yes! Data Architects at Ford Motor Company may have options for remote work. The company recognizes the need for flexibility and offers various work arrangements to support your professional journey while accommodating your work-life balance.

A Data Architect at Ford Motor Company can enjoy a comprehensive benefits package that includes immediate medical, dental, and vision coverage, flexible family care days, tuition assistance, a generous paid time off policy, and employee resource groups. Additional perks such as a vehicle discount program, paid holidays, and family building benefits also enhance the overall work experience.

When tackling data architecture design, I first assess the specific needs of the business and research the existing data sources. I prioritize understanding the workflows and data models that inform my architectural decisions. It's essential to use scalable, secure, and efficient technology stacks, while aligning with industry best practices.

Certainly! In my previous roles, I've developed and optimized ETL processes that support data extraction from various sources, transformation into a usable format, and loading into data warehouses. I'm adept at ensuring data integrity and performance, often utilizing tools like Apache NiFi and Talend for automation and monitoring.

Migrating legacy data environments involves meticulous planning and execution. I typically conduct a comprehensive analysis of current data structures and workflows, devise an incremental migration strategy to minimize disruptions, and thoroughly test each phase to ensure data integrity and performance post-migration.

Data security and compliance start with a solid architecture. I implement encryption, access controls, and regular audits to safeguard sensitive information. Additionally, I stay updated on regulations such as GDPR and HIPAA to ensure that all design elements comply with legal standards.

I have extensive experience building and managing cloud data solutions, particularly in GCP. I leverage services like BigQuery for analytics, Cloud Storage for data lakes, and utilize Kubernetes for orchestration and scaling to handle large data sets without compromising performance.

Collaboration is vital in any data-centric role. I engage regularly with data scientists and analysts to understand their data needs, ensuring the architecture I design supports their analytical requirements. I foster open communication and encourage feedback to refine our data solutions continuously.

Performance bottlenecks can arise from various factors, such as inefficient queries or inadequate resource allocation. I start by performing in-depth monitoring and diagnostics to identify the root cause, followed by optimizing queries, redistributing workloads, or upgrading resources as necessary to enhance performance.

Mentoring junior developers involves encouraging them to take ownership of smaller projects while providing support. I focus on sharing knowledge of best practices, conducting code reviews, and facilitating learning opportunities that enable them to grow and take on more complex tasks within data architecture.

I prefer using Git for version control due to its robustness and flexibility. For CI/CD, I frequently opt for platforms like GitHub Actions and Jenkins to automate deployment processes, streamline testing, and ensure high code quality throughout the development lifecycle.

In a previous project, I led the design and implementation of a real-time data pipeline that integrated disparate data sources into a unified data warehouse. This solution significantly improved reporting capabilities and decision-making processes within the organization, leading to a 30% reduction in data retrieval time.