Director of Engineering
Lead and grow the Platform Engineering team building DataVisor's large-scale AI-based fraud and risk decision platform. Set technical vision for real-time/batch unified decisioning using big data, streaming, and agentic AI technologies while driving cross-functional execution and talent development.
About the job
What You’ll Do
- Define Strategy & Roadmap: Set the technical vision and execution roadmap for the Platform Engineering team in alignment with company priorities and long-term product strategy.
- Lead at Scale: Build, manage, coach, and grow a high-performing team of engineers and engineering leaders, fostering technical excellence, strong execution, and career development.
- Architect & Deliver: Oversee the design and delivery of a large-scale, AI-based fraud and risk decision platform that supports enterprise-grade real-time and batch decisioning.
- Drive Innovation in Fraud Detection: Partner across engineering and data science to apply unsupervised, supervised, and agentic AI methods to uncover and stop fraudulent behavior.
- Unify Decisioning: Lead the development of a real-time and batch unified decision platform that powers enterprise-scale fraud prevention.
- Scale Infrastructure: Guide the evolution of distributed, real-time data systems for low-latency, highly reliable decisioning.
- Leverage Big Data: Drive adoption and effective use of Spark, Flink, Cassandra, Kafka, and related technologies to support high-throughput ML and data pipelines.
- Partner Cross-Functionally: Work closely with product, infrastructure, security, customer-facing teams, and executive leadership to align engineering delivery with business needs.
- Raise the Bar: Establish strong engineering processes, operational rigor, architecture reviews, and high-quality development practices across the organization.
- Hire & Develop Talent: Attract top engineering talent and build an inclusive, high-accountability culture that supports growth, ownership, and innovation.
Requirements
- BS degree in Computer Science or related field required; MS/PhD preferred.
- 10+ years of software development experience, including substantial experience leading engineering teams and organizations.
- 5+ years of experience managing teams, with demonstrated success leading senior engineers and/or engineering managers.
- Fluent in Java or C++ programming, with knowledge of Python; strong background in system design, architecture, and scalable distributed systems.
- Solid understanding of AI/ML concepts (unsupervised, supervised, and emerging approaches such as agentic AI); experience applying ML in production systems is strongly preferred.
- Familiarity with modern AI systems, including LLM-powered applications, model evaluation, prompt-driven workflows, and agent-based system design.
- Experience leading engineering teams that build AI-enabled products, data-intensive platforms, or ML infrastructure is a strong plus.
- Experience leading large-scale platform or infrastructure initiatives in high-growth or technically complex environments.
- Familiarity with relational databases, SQL, and ORM frameworks (JPA, Hibernate) is a plus.
- Experience with big data technologies (Cassandra, Flink, Spark, Kafka) preferred.
- Knowledge of the Spring Framework is a plus.
- Strong track record of operational excellence, cross-functional collaboration, and execution against ambitious goals.
- Excellent oral and written communication skills, including the ability to influence across technical and business audiences.
- Strong team spirit, sound judgment, and a leadership mindset grounded in collaboration and accountability.
Benefits
- Compensation: Annual salary range of USD $200,000 – $380,000, commensurate with experience.
- Impact at Scale: Work on fraud detection systems processing billions of events daily.
- Cutting-Edge Tech: Push the frontier in streaming, agentic AI, and big data infrastructure.
- Career Growth: Lead a high-impact team with opportunities to shape technical direction and grow into senior leadership.
Skills
Java, C++, Python, Spark, Flink, Cassandra, Kafka, Spring Framework, SQL, AI/ML, LLMs, Distributed Systems
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