Data Team Lead

Orca AI is revolutionizing maritime operations with cutting-edge computer vision, augmented reality, and autonomous technologies. Our mission is bold: make ocean shipping safer, more efficient, and significantly greener.

From real-time navigation assistance to AI-driven insights that reduce fuel consumption and prevent collisions, we’re building a future where ships sail smarter. Join our growing team and help shape the next generation of intelligent maritime systems used by leading fleets across the globe.

We’re seeking an experienced and visionary Data Team Lead to take end-to-end data ownership, managing teams of engineers and analysts, from high-level planning and architectural design to on-going engineering management and strategic cross-functional collaboration.

As we evolve Perceptional AI to Reasoning and autonomous AI initiatives, at orcas solutions deployed at thousands of maritime vessels (cargo, cruising, tankers and more).

Responsibilities:

• Lead, mentor, and grow high-performing data teams.
• Define and maintain a forward-looking data roadmap aligned with the company AI and product strategy.
• Build and maintain work plans that balance tech debt reduction, legacy migrations, alongside next gen. product deliveries.
• Act as the central sync point between the Data teams and key stakeholders: Product, CSMs, UI/UX, R&D, Algorithm, System, and Infra.
• Identify, prioritize, and resolve cross-team dependencies and roadblocks with a proactive, solution-oriented mindset.
• Own and evolve the data platform architecture, including modeling, pipes and services.
• Drive the modernization of the stack: enable Gen AI, serverless architectures, and scalable lakehouse and data processing patterns.
• Overlook best practices put forward by the team: design patterns, code reviews, CI/CD, tests, infras management (e.g. aws cdk), APIs to other R&D teams.

Requirements

• 6+ years of data engineering experience
• 2+ years of experience in leading data teams
• Proven track record in managing cross-functional dependencies, roadmaps, and planning.
• Deep understanding of big data architectures, modeling and processing design patterns.
• Experience with cloud-native (e.g., AWS) infrastructures.
• Experience modernizing legacy data systems.
• Familiarity with OSS Lakehouse architectures (Delta/Iceberg).
• Familiarity with event-driven systems.