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