Data Engineer,DIgital Analytics and Experimentation

We are seeking an experienced Data Engineer Digital Analytics to join Fords Digital Analytics & Experimentation team. This role will design build validate and scale the data foundations that power trusted digital measurement KPI reporting experimentation AI-enabled analytics and enterprise digital data products across the broader DAE portfolio.

Working closely with digital product analytics tagging data platform and business stakeholders across Digital Analytics & Experimentation you will translate analytical and product requirements into resilient data pipelines reusable data assets automated validation frameworks and well-documented data models that support Fords global digital ecosystem.

The strongest candidate will bring deep technical fluency in data engineering strong digital analytics context and practical experience applying AI tools automation and modern development practices to improve the speed quality and scalability of analytics delivery.

• Design build and maintain scalable data pipelines and curated data assets that support digital analytics KPI reporting experimentation personalization and AI-enabled insight generation.

• Partner with analytics leads product managers tagging teams engineers and data platform partners to translate measurement needs and stakeholder questions into reliable technical requirements data models and delivery plans.

• Integrate query transform and validate clickstream customer vehicle lead dealer campaign CRM product and other approved enterprise datasets to create trusted views of digital journeys and business outcomes.

• Develop reusable data models semantic layers feature-ready datasets and governed data products that enable Power BI reporting self-service analytics advanced analysis and AI-assisted analytics workflows.

• Implement automated data quality checks reconciliation routines anomaly detection and pipeline monitoring to improve confidence in digital data and reduce manual validation effort.

• Support digital measurement quality by partnering with analytics and implementation teams to validate data capture diagnose downstream issues and ensure datasets reflect intended business logic and KPI definitions.

• Use modern engineering practices including version control code reviews documentation testing orchestration CI/CD and release governance to improve reliability and maintainability of analytics data products.

• Apply AI tools and frameworks responsibly to accelerate development automate repetitive engineering tasks summarize data quality findings assist with code generation and explore new AI-native analytics capabilities.

• Collaborate globally across regions and time zones to align data engineering work with common KPI definitions reporting standards experimentation practices and Digital Analytics & Experimentation operating models.

• Communicate technical concepts clearly to analytical product and business audiences helping stakeholders understand data lineage quality considerations tradeoffs and recommended approaches.

• Bachelors degree in Computer Science Data Engineering Data Science Information Systems Engineering Statistics Mathematics or a related field or an equivalent combination of education and experience.

• 5 years of experience in data engineering analytics engineering business intelligence engineering digital analytics engineering or a related technical data role.

• 3 years of hands-on experience using SQL to build transform join validate and optimize large-scale datasets.

• Experience building and maintaining data pipelines data models ETL/ELT workflows APIs orchestration jobs or production analytics datasets in a cloud or enterprise data environment.

• Experience with Python PySpark dbt Airflow Dataflow Dataform or comparable data engineering tools and frameworks.

• Experience supporting digital analytics product analytics customer analytics marketing analytics experimentation or self-service reporting use cases.

• Demonstrated ability to document data lineage business logic technical assumptions validation rules and data quality considerations for cross-functional audiences.

• Strong written and verbal communication skills including the ability to work effectively with analytics product engineering and business partners across global teams.

• Hands-on experience with Google Cloud Platform BigQuery Cloud Composer Cloud Functions Pub/Sub Dataproc Snowflake Databricks or similar cloud data platforms.

• Experience designing governed reusable data products semantic models certified datasets and reporting-ready tables for Power BI analytics and business intelligence teams.

• Experience with GitHub Jira release management technical design reviews QA processes and production support for data pipelines or analytics products.

• Practical fluency with AI-assisted development tools code generation natural language query experiences prompt-driven analysis or AI-enabled workflow automation.

• Experience using AI responsibly to accelerate data engineering delivery improve documentation detect data quality issues generate test cases summarize pipeline health or support analytical product development.

• Exposure to machine learning feature engineering vector databases embeddings retrieval-augmented generation or LLM-powered analytics solutions is a plus.

• Ability to evaluate when AI can improve speed and scalability while maintaining appropriate controls for data quality privacy security and human review.

• Working knowledge of Adobe Analytics Google Analytics web and app instrumentation event schemas funnel measurement attribution and digital journey analytics; familiarity with tag management is helpful but not required as a primary focus.

• Experience combining digital behavioral data with customer vehicle lead dealer campaign CRM retail funnel transactional or product data.

• Experience supporting experimentation personalization Adobe Target A/B testing data pipelines test instrumentation or measurement validation.

Retail eCommerce automotive or omnichannel customer journey experience especially in complex regional or global digital environments