Senior Data Engineer

Company Overview 

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 Senior Data Engineer to design, build, and operate the data pipelines and platforms that power analytics, AI, and product work. This role is ideal for an engineer with several years of experience who can confidently own pipelines end-to-end and bring strong technical judgment to the problems they take on. 

In this role, you will work as a key contributor on a cross-functional team, designing scalable pipelines, building and managing data warehouses and data lakes, and integrating data from across systems. You’ll collaborate with experienced engineers to enhance existing systems, deliver new capabilities, and contribute to the technical decisions that shape how our data work scales over time. 

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 

• Design and implement scalable data pipelines, taking ownership of features end-to-end 

• Build and manage data warehouses and data lakes 

• Optimize data processing workflows and integrate data from multiple systems 

• Implement data modeling and schema design that supports analytics and downstream use 

• Implement data validation and monitoring to ensure data quality, reliability, and availability 

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

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

Collaboration & Communication 

• Collaborate closely with analytics, AI, and product teams to align data work with broader product and business goals 

• Communicate technical tradeoffs and decisions clearly across functions, translating data architecture and constraints into terms others can act on 

• Participate actively in code reviews and design discussions, raising concerns and offering constructive feedback 

Leadership & Influence 

• Contribute to data architecture decisions, bringing thoughtful perspective on tradeoffs and long-term implications 

• Take ownership of meaningful pieces of work and follow through end-to-end, including the unglamorous parts of operating data systems 

• Support data governance and compliance efforts, helping raise the bar on practices across the team 

 What You’ll Bring 

• 5–7 years of professional data engineering experience 

• Strong SQL and Python or Scala skills 

• Working knowledge of cloud data platforms and enterprise data warehousing 

• Hands-on experience with ETL/ELT frameworks and pipeline orchestration 

• Experience with streaming platforms and real-time data processing 

• Strong data modeling skills, including warehouse and lake schema design and data transformation 

• Working knowledge of distributed systems