Staff Data Engineer

Robots & Pencils is an applied AI engineering firm building the next frontier of business architecture. We design and ship AI co-workers that integrate into enterprise operations and deliver measurable results for our clients. We're all in on AWS, combining deep UX capability with senior engineering talent to get AI into production fast and keep it there. 
We’ve earned the trust of leaders across Consumer Products and Retail, Education, Energy, Financial Services, Healthcare, and Manufacturing and more, and earned a reputation as the nimble alternative to traditional global systems integrators. Founded in 2009, with delivery centers in Canada, the United States, Eastern Europe, and Latin America, we are smaller, faster, and more senior by design. Our teams average 15+ years of experience. We move fast, sweat the details, and build things that actually ship. 

Position Overview 

We’re looking for a Staff Data Engineer to join a multi-disciplinary engineering team building modern, enterprise-grade data platforms. This role is ideal for an experienced engineer who can define data strategy, own platform decisions end-to-end, and contribute to technical leadership across the team. 

In this role, you will design scalable data lakes, warehouses, and pipelines, define governance and quality standards, and drive data platform modernization across real, in-flight work where performance, reliability, and security are critical. You’ll mentor more junior engineers, partner with leadership on data strategy, and bring an AI-forward mindset.  

Why This Role Matters 

At Robots & Pencils, we design AI systems for a human world. Our name says it all. Robots and pencils means engineering paired with creativity, because every agent we ship has to work for real people in real workflows. That balance is baked into how we operate. 
 
Every role here contributes directly to that mission. Here, you shape how AI systems integrate into enterprise operations, how teams move at real velocity, and how products create measurable impact for clients and the people they serve. We ship production-ready AI in 30 to 45 days. That pace demands people who take ownership, lead with craft, and care deeply about what they put their name on. 

What You’ll Do 

Craft & Delivery 

• Define data architecture and platform strategy, leading design across pipelines, warehouses, and data lakes 

• Build and optimize scalable data pipelines supporting batch and real-time processing 

• Define and enforce data governance, quality standards, and compliance frameworks across the platform 

• Build monitoring, logging, and alerting for data pipelines and services, and contribute to CI/CD workflows for data deployment and automation 

• Drive data platform modernization, optimizing for performance, cost, and scalability 

• Bring an AI-forward mindset to your daily work, using tools like Claude, Cursor, and other modern AI assistants to ship higher-quality work at pace 

• Design and implement data contracts and event flows in collaboration with backend, platform, and engineering teams  

• Lead the design and implementation of data pipelines for production AI/ML systems, including embeddings, vector stores, RAG data preparation, feature stores, and training/inference data flows 

• Integrate data services with APIs, middleware, and third-party systems to support end-to-end data consumption 

Collaboration & Communication 

• Partner with leadership on data strategy, translating technical depth into decisions others can act on 

• Collaborate closely with engineering, analytics, AI, and product teams to align data platforms with broader goals 

• Advocate for data quality, governance, and platform best practices across teams and engagements 

Leadership & Influence 

• Establish data engineering standards that lift the quality and consistency of work across the team 

• Mentor junior and mid-level engineers, helping them grow their craft, confidence, and impact 

• Make high-stakes architectural decisions with clear ownership and consideration of long-term tradeoffs 

What You’ll Bring 

• 7+ years of professional data engineering experience, with experience leading complex data platform initiatives 

• Strong system architecture background with expertise in distributed data systems 

• Expert proficiency in Python, Scala, and SQL 

• Deep expertise with cloud-native data platforms and enterprise data warehousing 

• Strong expertise in data pipeline orchestration and processing  

• Strong experience with streaming platforms and real-time data processing (e.g., Kafka, Kinesis, Pub/Sub) 

• Strong data modeling expertise and experience with data transformation 

• Strong experience with data quality, governance, and compliance frameworks 

• Strong experience with container orchestration and CI/CD for data systems 

• Strong experience building data pipelines for production AI/ML systems, including embeddings, vector stores, RAG data preparation, feature stores, and training/inference data flows