Data Engineer III

Job description

**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. **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. **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