Data Engineer III
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Data Engineer – Marketing Optimization Capabilities & Analytics (MOCA)
About the Team
Marketing Optimization Capabilities & Analytics (MOCA) is an enterprise Marketing Mix Modeling (MMM) platform that enables American Express to measure and optimize the impact of marketing investments. MOCA separates short-term advertising-driven contributions from long-term business trends, external factors, and seasonal influences to provide actionable insights for marketing decision-making.
The platform models marketing drivers (inputs) and acquisition outcomes at the product, response channel, and DMA level on a weekly basis, helping leadership understand the effectiveness and ROI of enterprise marketing spend.
Role Summary
We are seeking a highly motivated Senior Data Engineer to join the MOCA team. This role will be responsible for designing, building, and maintaining scalable data pipelines and analytical data products that power enterprise-level Marketing Mix Modeling and marketing analytics capabilities.
The ideal candidate will have strong expertise in big data engineering, cloud technologies, data modeling, and large-scale ETL development. The role requires close collaboration with data scientists, product managers, marketing analytics teams, and business stakeholders to deliver reliable and scalable data solutions.
Responsibilities
Key Responsibilities
- Design, develop, and maintain scalable data pipelines supporting MOCA and MMM workloads.
- Build and optimize batch and near-real-time data ingestion processes from multiple enterprise data sources.
- Develop and maintain data models supporting marketing analytics, attribution, experimentation, and reporting use cases.
- Partner with Data Science teams to operationalize Marketing Mix Models and analytical outputs.
- Design and implement data quality, monitoring, lineage, and governance frameworks.
- Build reusable data services, APIs, and datasets to support enterprise analytics and reporting.
- Optimize data processing performance, scalability, and reliability across large datasets.
- Collaborate with Product, Marketing, and Analytics teams to translate business requirements into technical solutions.
- Support cloud migration and modernization initiatives across the analytics ecosystem.
- Mentor junior engineers and promote engineering best practices.
Qualifications
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field.
- 5+ years of experience in Data Engineering or related disciplines.
- Strong SQL skills and experience working with large-scale analytical datasets.
- Expertise in Python, Spark, Scala, or Java.
- Experience building enterprise-grade ETL/ELT pipelines.
- Strong understanding of dimensional modeling, data warehousing, and data architecture principles.
- Experience working with cloud platforms such as AWS, Azure, or GCP.
- Experience with orchestration tools such as Airflow, Control-M, or similar platforms.
- Strong understanding of data quality, observability, and governance practices.
- Excellent communication and stakeholder management skills.
Preferred Qualifications
- Experience supporting Marketing Analytics, Customer Analytics, or Marketing Mix Modeling platforms.
- Experience working with large-scale customer acquisition and marketing datasets.
- Familiarity with machine learning operationalization and model deployment workflows.
- Experience with enterprise experimentation and measurement platforms.
- Experience with modern data lake and data mesh architectures.
- Experience leading technical initiatives and mentoring engineering teams.
What Success Looks Like
- Deliver scalable and reliable data pipelines supporting MOCA and MMM capabilities.
- Improve data quality, performance, and operational efficiency.
- Enable faster and more accurate marketing investment decisions through high-quality data products.
- Drive modernization and automation initiatives across the analytics ecosystem.
- Serve as a trusted technical partner for Data Science, Product, and Business stakeholders.
Key Technologies
- SQL
- Python
- Spark / PySpark
- Hadoop / Big Data Ecosystem
- Airflow
- Cloud Platforms (AWS / Azure / GCP)
- Data Warehousing
- ETL / ELT Frameworks
- Git / CI-CD
- Analytics & Reporting Platforms