Technical Product Manager - Cloud data platforms, AI/ML

Join our team to elevate your product management career in data engineering.

As a Technical Product Manager within the Credit Card Data & Analytics team, you will define and execute the strategy for our enterprise Card data platforms. This role will focus on building and scaling data infrastructure, data products, and cloud-native solutions that power analytics, AI/ML, and digital products across the organization.

Job Responsibilities:

• Define product vision, product requirements, and help execute the roadmaps for data platform products , including data lakes, ingestion pipelines, and real-time streaming.
• Partner with data owners, engineering, data science, and business stakeholders to deliver scalable, compliant data product solutions.
• Lead modernization initiatives, including migration from legacy systems to cloud-native architectures such as AWS and Delta Lake.
• Drive improvements in data quality, metadata governance, and compliance across platform services.
• Deliver measurable business impact through efficiency gains, cost savings, and enabling AI/ML use cases.
• Evangelize platform capabilities and influence adoption across internal teams.

• Minimum 10 years of tech product management experience, with 3+ years in data or platform products.
• Strong technical knowledge of cloud data platforms, including AWS, Databricks, Delta Lake, Hadoop, and Kafka.
• Experience in API-first product design, data governance, and real-time data processing.
• Proven success in delivering enterprise-scale data products with measurable business outcomes.
• Strong leadership and stakeholder management skills, with the ability to align across data owners , engineering, and business teams.
• Hands-on exposure to tools such as Snowflake and can make hands dirty .
• Ability to translate AI/ML capabilities into platform features that unlock new business opportunities.

• Familiarity with AI/ML platforms and workflows, including an understanding of Generative AI architectures such as Retrieval-Augmented Generation (RAG) and vector databases.
• Technical knowledge of data engineering, data pipelines, data modeling, and data architecture preferred.