Data Engineer,DIgital Analytics and Experimentation

We are seeking an experienced Data Engineer, Digital Analytics to join Ford’s 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 Ford’s 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.

• Bachelor’s 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.

Preferred Qualifications

Data Engineering & Cloud Platforms

• 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.

AI, Automation & Modern Analytics Delivery

• 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.

Digital Analytics & Business Context

• 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