Engineering Manager, Data Knowledge Platform Engineering

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Company Overview

A leading global financial technology organization is transforming how businesses manage payments and financial operations through a unified platform. Serving more than 250,000 businesses worldwide, the company provides integrated solutions across business accounts, payments, spend management, treasury, and embedded finance. With a global presence and a strong focus on innovation, the organization continues to invest in cutting-edge technologies, including AI, machine learning, and large-scale data platforms, to shape the future of global financial services.

The organization is seeking an Engineering Manager, Data Knowledge Platform Engineering to lead strategic initiatives across its data ecosystem. This role will oversee the development of scalable data and AI infrastructure, define long-term technical roadmaps, and drive cross-functional collaboration to support business objectives.

The successful candidate will be an experienced engineering leader with expertise in data platforms, distributed systems, and organizational scaling. This position offers the opportunity to influence the future of AI-enabled data infrastructure while mentoring high-performing engineering teams and delivering impactful business outcomes.

About the Team

The Knowledge Platform team is responsible for building the foundational infrastructure that enables data, AI, and machine learning capabilities across the organization. The team manages technologies spanning the entire data lifecycle, including distributed data processing, event streaming, AI/ML platforms, and governance systems.

Its mission is to evolve the company’s data ecosystem into an AI agent-ready infrastructure that empowers users to derive actionable insights through analytics, natural language interactions, and real-time decision-making capabilities.

Key Responsibilities

• Provide technical leadership and define a strategic 1–3 year roadmap for the real-time data platform.
• Lead large-scale modernization initiatives to introduce real-time analytical processing at petabyte scale.
• Partner with product teams to enable new data-driven capabilities, including AI-powered applications and real-time dashboards.
• Scale and structure engineering teams through hiring, mentoring, and leadership development initiatives.
• Serve as a trusted advisor to product and engineering stakeholders on data and AI-related technologies.
• Establish best practices for communication, collaboration, and execution across multiple engineering teams.
• Drive alignment between technical initiatives, business priorities, and long-term organizational goals.

Required Qualifications

• Minimum of 8 years of experience in data or software engineering, including at least 3 years in a leadership or management capacity.
• Proven experience managing and scaling engineering organizations consisting of 10+ team members, including managers and senior individual contributors.
• Demonstrated success in defining and executing technical strategies that deliver measurable business or operational impact.
• Strong expertise in data architecture, software engineering, and platform strategy.
• Deep knowledge of distributed data processing technologies such as Apache Spark and Databricks.
• Experience with event streaming technologies, including Kafka.
• Solid understanding of modern data storage and serving technologies, including CubeJS, Elasticsearch, ClickHouse, and similar platforms.
• Familiarity with observability and monitoring tools such as Splunk, Grafana, and Prometheus.

Preferred Qualifications

• Experience working within the financial services or fintech industry.
• Hands-on experience designing data processing patterns for modern Lakehouse architectures.
• Excellent written and verbal communication skills, with the ability to communicate effectively across technical and executive audiences.
• Ability to rapidly assess emerging technologies and conduct proof-of-concept initiatives to inform architectural decisions.
• Experience succeeding in fast-paced and complex environments.

Disclaimer

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