Data Scientist
Summary
Builds and maintains cloud-based data pipelines, warehouses, and AI/ML-ready datasets for Ford’s global analytics and AI initiatives, focusing on scalability, data quality, and GenAI infrastructure.
About Us:
In Global Data Insight & Analytics (GDI&A), we harness the power of data and artificial intelligence to navigate Ford Motor Company through the disruptiveness of the information age. We're a team of innovators who strive to realize the enterprise's goals, reveal hidden opportunities, and achieve data superiority.
About You:
We are looking for a hands-on Data Engineer with 3+ years of experience building production-grade data pipelines, cloud data platforms, and automated data workflows. You are comfortable working across structured, semi-structured, and unstructured data; you understand the importance of data quality, lineage, security, and cost optimization; and you are excited to build the data foundation required for modern AI, ML, and GenAI use cases.
Understand business, analytics, and AI use cases and translate them into scalable data engineering solutions.
Design, build, and maintain reliable batch and streaming data pipelines for ingestion, transformation, validation, and publishing.
Develop curated, reusable, and well-documented data products that support BI dashboards, analytics applications, ML models, and GenAI-enabled solutions.
Implement strong data quality checks, observability, lineage, metadata management, and monitoring practices to improve trust in enterprise data assets.
Write clean, modular, and well-tested code using Python, SQL, and modern data engineering frameworks.
Use cloud-native technologies such as BigQuery, Dataflow, Dataproc, Cloud Composer/Airflow, Dataform, DBT, Spark, or equivalent tools to deliver resilient data solutions.
Enable AI/ML and GenAI teams by preparing high-quality feature datasets, vector-ready datasets, document corpora, and governed data access patterns.
Partner with data scientists, ML engineers, product owners, and business stakeholders to support experimentation, model deployment, and production analytics.
Apply DataOps practices including CI/CD, version control, automated testing, reusable templates, release management, and production support standards.
Optimize pipeline performance, storage usage, compute cost, and reliability across cloud-based data platforms.
Support data governance, privacy, access control, and compliance expectations for enterprise and AI-ready data assets.
Stay current with advances in cloud data engineering, AI data infrastructure, orchestration, data quality, and GenAI-enabling technologies.
Minimum Qualifications:
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, Engineering, Statistics, Mathematics, or related technical field.
3+ years of hands-on experience in data engineering, ETL/ELT development, data warehousing, or cloud-based data platform delivery.
Strong proficiency in SQL and Python for data extraction, transformation, automation, testing, and production support.
Experience designing and operating scalable pipelines on cloud platforms such as Google Cloud Platform, AWS, Azure, or equivalent enterprise data ecosystems.
Experience with modern data platforms and tools such as BigQuery, Spark, Dataflow, Dataproc, Airflow/Cloud Composer, Dataform, DBT, or similar technologies.
Good understanding of data modeling, dimensional modeling, partitioning, clustering, performance tuning, and cost optimization.
Working knowledge of data quality frameworks, monitoring, alerting, metadata, lineage, and production support practices.
Familiarity with Git, CI/CD, agile delivery, code reviews, documentation, and reusable engineering standards.
Strong communication skills with the ability to explain technical solutions clearly to engineering, analytics, and business stakeholders.
Preferred Qualifications:
5+ years of experience delivering enterprise data engineering solutions in cloud-native environments.
Experience building data products for AI/ML, GenAI, semantic search, retrieval-augmented generation, feature engineering, or model monitoring use cases.
Experience working with unstructured data such as documents, logs, text, images, transcripts, or embeddings, and preparing them for downstream AI consumption.
Hands-on experience with DataOps, MLOps enablement, pipeline observability, automated testing, and production incident resolution.
Experience migrating legacy workflows from Hadoop, Alteryx, or on-premise platforms to modern cloud services.
Experience with APIs, microservices, event-driven architectures, streaming data, or real-time analytics.
Cloud certifications in Google Cloud Platform, AWS, Azure, or relevant data engineering technologies.
Experience mentoring junior engineers, defining engineering standards, or contributing reusable platform accelerators.