JOBWAVE VERIFIED

Data Engineer — Digital Analytics & Experimentation

Company: Ford Motor Company
Team: Digital Analytics & Experimentation (DAE)
Role Title: Data Engineer, Digital Analytics and Experimentation
Location: Chennai, Tamil Nadu, India
Work Mode: Hybrid
Employment Type: Not specified
Experience Required: 5+ years in data engineering or a related technical data role
SQL Experience: 3+ years of hands-on experience
Education: Bachelor's degree in a relevant field or equivalent combination of education and experience
Salary: Not specified
Job ID: Not specified
Posting Status: Trending

Core Technology Stack

• Programming and Querying: SQL, Python, PySpark
• Data Warehousing: BigQuery, Snowflake, Databricks
• Data Transformation: dbt, Dataform
• Data Processing: Dataflow, Dataproc
• Orchestration: Airflow, Cloud Composer
• Cloud Services: Google Cloud Platform (GCP), Cloud Functions, Pub/Sub
• Analytics and BI: Power BI, semantic models, reporting-ready datasets
• Digital Analytics: Adobe Analytics, Google Analytics, event schemas, funnel measurement, attribution
• Experimentation: A/B testing, Adobe Target, measurement validation
• Engineering Practices: GitHub, CI/CD, automated testing, release governance, documentation
• AI and Automation: AI-assisted development, code generation, workflow automation, AI-enabled analytics
• Advanced AI — Preferred: Feature engineering, vector databases, embeddings, RAG, LLM-powered analytics

The listed platforms span required experience and preferred qualifications; the description does not indicate that every tool is mandatory.

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JOBWAVE INSIGHTS

1. Who Is This Role For?

This position is suited to an experienced Data Engineer or Analytics Engineer who can build reliable data foundations for digital analytics, reporting, experimentation, and AI-enabled business insights.

The role combines three major areas:

1. Data Engineering: Building scalable pipelines, transformations, reusable datasets, and data quality frameworks.
2. Digital Analytics: Supporting clickstream data, customer journeys, KPI measurement, marketing analytics, and experimentation.
3. Modern Analytics Delivery: Applying cloud platforms, automation, AI-assisted engineering, and strong software development practices.

This is not purely a dashboard-development or traditional ETL role. The candidate is expected to understand how digital events are captured, transformed, validated, and converted into trustworthy business metrics.

2. Key Skills

Skill	Priority	What You Need to Demonstrate
SQL	Critical	Complex transformations, joins, validation, and optimization
Data Engineering	Critical	Production-grade pipelines and curated data assets
Data Modeling	Critical	Reusable, documented, reporting-ready datasets
Python	High	Data processing, automation, and validation
ETL/ELT	Critical	Reliable ingestion and transformation workflows
Cloud Data Platforms	High	Experience in enterprise or cloud data environments
Data Quality	Critical	Automated checks, reconciliation, anomaly detection
Digital Analytics	High	Clickstream, events, KPIs, funnels, and customer journeys
dbt / Dataform	Strong advantage	Modular, tested data transformations
Airflow / Cloud Composer	Strong advantage	Pipeline scheduling and orchestration
BigQuery	Strong advantage	Cloud data warehousing and analytical SQL
PySpark / Dataflow / Dataproc	Strong advantage	Large-scale data processing
Power BI	High	Supporting BI teams with trusted datasets and semantic models
GitHub and CI/CD	High	Version control, testing, and controlled releases
AI-Assisted Engineering	High-value differentiator	Responsible use of AI for development and automation
Adobe / Google Analytics	Preferred	Digital measurement and event-data context
Experimentation	Preferred	A/B testing pipelines and measurement validation
RAG / Embeddings / Vector Databases	Optional advantage	AI-powered analytics and retrieval workflows

3. Priority Skills

Highest priority

• SQL and large-scale dataset transformation
• Data pipelines, ETL/ELT, and data modeling
• Python and practical data engineering
• Data validation, reconciliation, and anomaly detection
• Production analytics datasets and cloud/enterprise data environments
• Documentation, data lineage, and business logic
• Five or more years of relevant experience, including three or more years of hands-on SQL

Strong advantages

• BigQuery and GCP data services
• dbt, Dataform, Airflow, and Cloud Composer
• Digital analytics and clickstream data
• Power BI-ready datasets and semantic layers
• GitHub, CI/CD, automated testing, and release management
• AI-assisted development and workflow automation
• Experimentation, A/B testing, and digital measurement

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JOB DETAILS

Role Overview

The Digital Analytics & Experimentation team supports Ford's global digital ecosystem by delivering trusted measurement, KPI reporting, experimentation capabilities, and analytics data products.

As a Data Engineer, you will translate analytical and product requirements into reliable pipelines, reusable data models, governed data assets, and automated quality controls.

You will collaborate with analytics leads, product managers, tagging teams, data platform engineers, and business stakeholders across global regions.

Key Responsibilities

1. Data Pipeline Development

• Design, build, and maintain scalable data pipelines.
• Integrate, transform, and validate datasets from multiple enterprise sources.
• Deliver curated data assets for reporting, experimentation, personalization, and AI-enabled analytics.
• Support reliable and maintainable ETL/ELT workflows.

2. Digital Analytics Data Integration

Work with approved datasets involving:

• Clickstream and digital event data
• Customer and vehicle data
• Leads and dealer information
• Campaign and CRM datasets
• Product and transactional data
• Retail funnel and customer journey information

Combine these sources to produce reliable views of digital behavior and business outcomes.

3. Data Modeling and Analytics Enablement

• Develop reusable data models and semantic layers.
• Build reporting-ready tables and certified datasets.
• Support Power BI dashboards, self-service analytics, and advanced analysis.
• Maintain consistent KPI definitions and business logic.

4. Data Quality and Measurement Validation

• Implement automated data quality checks and reconciliation routines.
• Detect anomalies and monitor pipeline health.
• Validate digital measurement data against intended business rules.
• Investigate data capture and downstream transformation issues.
• Reduce manual validation effort and improve confidence in reporting.

5. Engineering Standards and Governance

• Use version control, code reviews, testing, and CI/CD.
• Document data lineage, technical assumptions, validation rules, and business definitions.
• Follow release governance and quality assurance processes.
• Improve pipeline reliability, maintainability, and production support.

6. AI and Engineering Automation

• Use AI-assisted development tools to accelerate coding and repetitive engineering work.
• Explore automated documentation and test generation.
• Apply AI to summarize data quality findings and pipeline health.
• Investigate AI-enabled analytics capabilities while maintaining privacy, security, and human oversight.

7. Experimentation and Digital Measurement

• Support data pipelines for experimentation and personalization.
• Work with analytics teams on event instrumentation and measurement validation.
• Enable A/B testing analysis and consistent KPI measurement.
• Help ensure that digital datasets reflect the intended business definitions.

8. Global Collaboration

• Coordinate with teams across regions and time zones.
• Align data models, reporting standards, and KPI definitions.
• Communicate data limitations, lineage, quality issues, and technical trade-offs clearly.

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REQUIREMENTS

Mandatory Qualifications

• Bachelor's degree in a relevant field or equivalent education and experience.
• At least 5 years of experience in data engineering, analytics engineering, BI engineering, digital analytics engineering, or a related technical data role.
• At least 3 years of hands-on SQL experience.
• Experience building and maintaining data pipelines, data models, ETL/ELT workflows, APIs, orchestration jobs, or production analytics datasets.
• Experience with Python, PySpark, dbt, Airflow, Dataflow, Dataform, or comparable engineering tools and frameworks.
• Experience supporting digital analytics, product analytics, customer analytics, marketing analytics, experimentation, or self-service reporting.
• Ability to document data lineage, business logic, assumptions, validation rules, and data quality considerations.
• Strong communication and cross-functional collaboration skills.

Preferred Qualifications

Data Engineering and Cloud

• GCP and BigQuery.
• Cloud Composer, Cloud Functions, Pub/Sub, and Dataproc.
• Snowflake or Databricks.
• Governed data products and reusable semantic models.
• Power BI reporting datasets.
• GitHub, Jira, release management, QA, and production support.

AI, Automation, and Modern Analytics

• AI-assisted coding and development.
• Code generation and automated test creation.
• Natural-language query experiences and prompt-driven analysis.
• AI-supported documentation and data quality investigation.
• Machine learning and feature engineering.
• Vector databases, embeddings, RAG, and LLM-powered analytics.
• Responsible AI practices, including data privacy, security, and human review.

Digital Analytics and Business Context

• Adobe Analytics or Google Analytics.
• Web and app instrumentation, event schemas, and tag management.
• Funnel measurement, attribution, and digital journey analytics.
• Customer, vehicle, dealer, campaign, CRM, and retail funnel datasets.
• A/B testing, Adobe Target, experimentation pipelines, and measurement validation.
• Retail, e-commerce, automotive, or omnichannel customer journey experience.

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POTENTIAL GAPS TO CHECK

1. SQL depth: Can you demonstrate advanced SQL across large datasets, including joins, window functions, optimization, and reconciliation?
2. Pipeline ownership: Have you built and maintained production pipelines rather than only querying existing tables?
3. Cloud data engineering: Have you worked hands-on with BigQuery, GCP, Snowflake, Databricks, or comparable platforms?
4. Transformation frameworks: Do you have practical experience with dbt, Dataform, or a comparable transformation framework?
5. Orchestration: Have you scheduled, monitored, and troubleshot production data workflows?
6. Digital analytics: Do you understand clickstream events, event schemas, funnels, attribution, and digital KPIs?
7. Data quality: Can you explain how you detect missing events, duplicates, schema changes, unexpected metric shifts, and pipeline failures?
8. Data modeling: Have you created reusable analytical datasets and clearly documented business definitions?
9. AI-assisted engineering: Can you show responsible, practical uses of AI for coding, testing, documentation, or validation?
10. Experimentation: Have you supported A/B testing data, personalization, or measurement validation?

Most important distinction: A candidate with strong traditional ETL experience may still need to demonstrate digital analytics context. Conversely, a digital analyst who primarily builds dashboards may need stronger pipeline engineering, SQL optimization, and production delivery experience.

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INTERVIEW PREPARATION

Phase 1 — SQL and Data Transformation

Prepare for:

• Complex joins, CTEs, and window functions
• Deduplication and event-level data processing
• Aggregations and KPI calculations
• Sessionization and customer journey analysis
• Incremental data processing
• Handling late-arriving and duplicated events
• Query performance and partitioning strategies
• Data reconciliation and validation
• Debugging discrepancies between source systems and dashboards

Phase 2 — Data Engineering and Cloud Platforms

Focus on:

• ETL versus ELT
• Designing reliable data pipelines
• Data modeling and dimensional modeling
• dbt models, tests, dependencies, and documentation
• Dataform and transformation workflows
• Airflow or Cloud Composer orchestration
• BigQuery optimization and partitioning
• Batch versus streaming processing
• Data ingestion, schema changes, and pipeline recovery
• Monitoring, retries, and failure handling

Be ready to explain which tools you have personally used and how you designed the corresponding production workflow.

Phase 3 — Digital Analytics and Experimentation

Review:

• Clickstream event structures
• Event schemas and instrumentation validation
• Sessions, users, events, and conversions
• Funnel analysis and customer journeys
• Campaign attribution and marketing performance
• Joining behavioral data with customer or CRM datasets
• KPI definitions and metric consistency
• A/B testing data requirements
• Experiment exposure, conversion events, and data quality
• Diagnosing missing events or unexpected conversion changes

Phase 4 — Data Quality, AI, and Production Operations

Prepare for:

• Automated data quality tests
• Reconciliation and anomaly detection
• Data lineage and metadata documentation
• Schema validation and contract changes
• Pipeline monitoring and incident response
• CI/CD for data transformations
• AI-assisted code generation and test creation
• Responsible handling of sensitive customer data
• Evaluating AI-generated code before production use
• Human review, auditability, and validation controls

Final Interview Focus

Prepare one end-to-end project that demonstrates how you turn raw digital or business data into a trusted analytics product.

Be ready to explain:

1. Business requirements and KPI definitions.
2. Source datasets and data ingestion.
3. Data transformations and modeling.
4. SQL and Python implementation.
5. Data quality tests and reconciliation.
6. Orchestration and cloud deployment.
7. Reporting or analytics consumption.
8. Monitoring, lineage, and production support.
9. AI or automation improvements, if applicable.
10. Measurable improvements in reliability, delivery speed, or data quality.

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PORTFOLIO PROJECT RECOMMENDATION

Digital Customer Journey Analytics Platform

Build a demonstration platform that transforms website or application events into trustworthy customer journey analytics.

Suggested architecture

• Python and SQL for ingestion and transformations.
• BigQuery or another analytical database.
• dbt or Dataform for reusable transformation models.
• Airflow or Cloud Composer for orchestration.
• Power BI for reporting.
• GitHub-based version control and automated testing.
• Data quality checks and pipeline monitoring.
• Optional AI-assisted documentation or anomaly investigation.

Suggested features

• Ingest synthetic clickstream events.
• Model users, sessions, page views, leads, and conversions.
• Build conversion funnels and campaign performance datasets.
• Reconcile event counts between source and curated tables.
• Detect missing events, duplicate records, and unusual metric changes.
• Publish documented KPI definitions and reporting-ready tables.
• Build a dashboard showing customer journeys and conversion rates.
• Add automated tests and pipeline failure alerts.
• Optionally use an LLM to summarize quality issues, with human review.

Why this project helps: It combines core data engineering with digital analytics, experimentation support, data governance, and modern automation—the central themes of this position.

Use synthetic or publicly available data, and clearly label any simulated findings.

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FINAL RECOMMENDATION

Overall fit: Strong for a Data Engineer or Analytics Engineer with advanced SQL, production pipeline experience, and a working understanding of digital analytics.

This role is especially relevant if you can combine cloud data engineering with event-based data, KPI reporting, automated validation, and stakeholder collaboration.

Application priority:

1. Demonstrate the required 5+ years of relevant experience and 3+ years of hands-on SQL.
2. Highlight production pipelines, cloud data platforms, data modeling, and data quality.
3. Show how you have supported analytics or reporting products, ideally involving digital behavior or experimentation.
4. Present practical examples of automation and responsible AI-assisted engineering.

The strongest differentiator is the ability to deliver trusted, governed, production-ready data products for global digital analytics, not merely to create queries or dashboards.