Engineering Manager, Data Knowledge Platform Engineering

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Company Overview

A leading global financial technology company provides unified payments and financial infrastructure for businesses worldwide. Its platform combines proprietary infrastructure and software to support business accounts, payments, spend management, treasury, and embedded finance at global scale.

The organization values entrepreneurial thinking, strong ownership, curiosity, collaboration, sound judgment, and the ability to move quickly while maintaining rigor. Employees are encouraged to use emerging technologies, including AI, to solve complex problems, create impactful products, and drive innovation.

The Knowledge Platform team plays a central role in the organization’s data and AI strategy. The team builds foundational infrastructure that enables the broader organization to leverage data, artificial intelligence, and machine learning to create business value. Its platforms support the complete data and AI/ML lifecycle while simplifying access and maintaining appropriate safety and governance.

The platform ecosystem includes data infrastructure such as Databricks, Spark, and Kafka, technologies for serving data through capabilities such as RAG and MCP, and platforms for hosting and governing AI/ML models. The team is also working toward an AI-agent-ready data ecosystem capable of providing actionable insights through analytics and natural-language querying while delivering robust real-time performance.

Key Responsibilities

• Provide visionary technical leadership and define a clear 1–3 year strategic roadmap for the Realtime Data Platform.
• Lead the multi-year modernization of the core data platform, including the introduction of real-time analytical processing at petabyte scale.
• Partner with product teams to enable data-driven capabilities such as AI-powered applications and real-time dashboards.
• Ensure the underlying platform infrastructure can support evolving product and business requirements.
• Scale and structure engineering teams through strategic hiring and organizational development.
• Mentor engineering managers and senior individual contributors.
• Establish effective communication and collaboration practices across multiple engineering teams.
• Build and maintain strong relationships with product and engineering organizations.
• Serve as a trusted technical advisor on data and AI-related initiatives.
• Define and execute technical strategies that contribute to significant business outcomes and operational improvements.
• Evaluate emerging technologies and guide architectural decisions through proof-of-concept initiatives when appropriate.

Required Qualifications

• At least 8 years of experience in data engineering or software engineering.
• At least 3 years of experience in a leadership or engineering management role.
• Proven experience managing and scaling engineering teams of 10 or more people, including managers and senior individual contributors.
• Demonstrated ability to define and execute technical strategies that have delivered significant business or operational outcomes.
• Strong expertise in data engineering and software engineering, particularly in architecture and technical strategy.
• Deep understanding of distributed data processing technologies such as Apache Spark and Databricks.
• Strong understanding of event-streaming technologies such as Kafka.
• Knowledge of modern data storage and serving technologies, including CubeJS, Elasticsearch, ClickHouse, and related technologies.
• Familiarity with observability tools such as Splunk, Grafana, and Prometheus.

Preferred Qualifications

• Experience working within the financial technology or financial services domain.
• Hands-on experience designing data processing patterns for modern Lakehouse architectures.
• Excellent written and verbal communication skills with the ability to communicate effectively with leadership, users, and company-wide audiences.
• Ability to rapidly evaluate technologies and conduct proof-of-concept initiatives to inform architecture decisions.
• Experience working effectively in complex and highly collaborative environments.

Disclaimer

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