Primary Function

As a Senior Data Engineer II – Technical Lead at Zoro, you will lead medium to large initiatives that span data platforms, data products, and AI-enabled systems. You will design and build cloud-native data pipelines, services, and interfaces that power analytics, machine learning, and AI agents across the organization.

This role owns the technical direction of the team’s solutions, with explicit accountability for technical debt, architectural integrity, and long-term scalability. You will play a central role in evolving Zoro’s data platform to support AI-first use cases, including feature generation, agentic workflows, and model-integrated data services.

As a technical leader, you will mentor engineers, influence platform strategy, and partner deeply with product, analytics, and machine learning stakeholders to translate business problems into durable data and AI solutions.

Duties and Responsibilities

• Technical Leadership: Collaborate with Technical Leads and Staff Engineers to define and execute the technical vision for Data Engineering, making architectural and tooling decisions that balance innovation, reliability, and long-term maintainability, including AI and agent enabled capabilities.

• Design and Architecture: Responsible for your team’s overall design and architecture of scalable, reliable, and high-performance data systems to support business intelligence, analytics, and data science initiatives.

• Data Pipeline Development: Build, optimize, and operate batch and streaming data pipelines that are observable, testable, cost-efficient, and explicitly designed to serve analytical, operational, and AI-driven workloads.

• AI Enablement and Agentic Systems: Enable and build AI-powered systems by using AI tools in daily engineering workflows, designing and integrating MCP servers or equivalent context layers, and creating AI agents that interact safely and reliably with data systems, APIs, and business processes.

• Software Craftsmanship: Champion high standards in software design, testing, CI/CD, and infrastructure-as-code, ensuring data and AI systems are secure, maintainable, and aligned with enterprise engineering standards.

• Data Governance and Trust: Embed data quality, lineage, access control, privacy, and correctness guarantees into data and AI architectures, establishing guardrails that ensure trustworthy and explainable use of data by humans and AI systems.

• Mentorship: Mentor and develop Data Engineers through technical guidance, design reviews, and hands-on coaching, fostering a culture of intellectual curiosity, engineering rigor, and continuous improvement.

Qualifications

• Education: Master’s degree in Computer Science, Applied Mathematics, Engineering, or other technology related field or an equivalent of this educational requirement in working experience is also acceptable

• Experience: Minimum of 7 years of experience in Data Engineering or a related field, with at least 3 years in a leadership or senior technical role.

• Technical Skills:

• AI & Agent Systems: Hands-on experience using AI tools as part of daily engineering workflows, designing or integrating MCP servers or equivalent context-serving systems, and building, deploying, or integrating AI agents with an understanding of model lifecycle concerns including training, inference, monitoring, and operational reliability.

• Emerging Capabilities: Experience designing or operating modern AI-adjacent data capabilities, including feature stores, vector databases, embedding pipelines, human-in-the-loop or agentic workflows, and an understanding of cost management and performance optimization for AI workloads.

• Software Engineering: Expert-level proficiency in at least one modern programming language with strong software design fundamentals.

• Data Modeling and Architecture: Expert-level experience designing analytical and operational data models.

• Cloud Platforms: Expert level experience with cloud-native infrastructure and managed data services.

• Data Pipelines: Expert level experience of batch and streaming architectures.

• Data Quality and Testing: Proven approaches to automated data validation and testing.

• Data Microservices: Experience building high-performance, data-backed APIs.

• Soft Skills:

• Problem-Solving: Excellent problem-solving skills and the ability to think critically and analytically.

• Communication: Excellent communication skills, with the ability to articulate complex technical concepts to non-technical stakeholders.

• Organization: Strong organizational skills and the ability to manage multiple projects and priorities in a fast-paced environment.

• Curiosity: A genuine curiosity about data and a proactive approach to exploring new tools and techniques.

• Leadership: Proven ability to lead, inspire, and mentor Data Engineers.

• Teamwork & Collaboration: Proven ability to form collaborative relationships with stakeholders across the organization.