Senior Data & AI Developer

Olson Kundig•Seattle, WA

4d•$120,000 - $150,000

About The Position

Olson Kundig is a design practice founded on the idea that the built environment can serve as a bridge between nature, culture, and people — and that inspiring surroundings have a positive effect on people’s lives. Our work spans the globe, from huts to high‑rises, private residences to museums, academic and civic projects, and places of worship. As the firm grows, so does the need for high‑quality data, analytics, and intelligent tools that support design excellence, operational clarity, and strategic decision‑making. We are seeking a Senior Data & AI Developer to play a critical, high‑impact role at the intersection of data infrastructure, business intelligence, applied AI, and organizational change. This is not a traditional reporting role — and not a purely experimental AI role. This position sits squarely in the middle: building trusted data foundations, deploying AI‑enabled workflows, and helping hundreds of professionals adopt new ways of working with confidence. You will report to the Director of Data Analytics and serve as a primary bridge between the firm’s Data & Analytics team and the broader organization — translating business needs into reliable, usable analytics and AI‑powered solutions. Success in this role depends as much on listening, communication, and judgment as on technical skill. The team is at an inflection point: Transitioning from traditional BI toward a purpose‑built AI ecosystem Undergoing a major ERP replacement Scaling analytics capabilities to meet growing firm‑wide demand This is a hands-on engineering role. You will write code, architect systems, configure cloud infrastructure, and deploy production AI applications. This role offers genuine influence over how data and AI are designed, governed, and used across the firm.

Requirements

Bachelor’s degree in Data Science, Information Systems, Computer Science, Business Analytics, or equivalent experience
5–8+ years of experience in data analytics, analytics engineering, business intelligence, or related roles with increasing scope and ownership
Advanced SQL skills, including query optimization and data modeling
Proficient Python developer: ETL/ELT pipelines, REST API backends, automation, and AI application development; experience with pandas, SQLAIchemy, FastAPI, and LangChain or similar.
Hands‑on Power BI experience (data modeling, DAX, deployment, and maintenance)
Experience architecting and building multi-system API integrations, including authentication flows (OAuth 2.0 / SSO/SAML) and webhook patterns.
Hands-on Git-based development workflows: branching, code review, CI/CD pipelines, testing, and production deployment.
Experience designing and implementing data lake or lakehouse architectures using structured and unstructured data sources (e.g., Delta Lake, Azure Data Lake Storage, S3, or equivalent).
Hands-on cloud deployment experience (Azure preferred, AWS, Snowflake, Databricks): compute, storage, managed databases, container services, and identity/access management
Experience working with messy, real‑world data and legacy systems
Hands-on experience building and deploying LLM applications: RAG pipelines, vector store management (Pinecone, pgvector, or Weaviate), embedding workflows, and API integration using Antropic, OpenAI, or Azure OpenAI SDKs
Experience implementing AI evaluation frameworks (e.g., LangSmith, Arize, or custom evals) to monitor model outputs, detect drift, and enforce output quality in production
Experience deploying AI applications to cloud infrastructure (Azure, AWS, or GCP), including containerization (Docker), serverless functions, and SSO/identity integration for enterprise tool rollout
Strong listening skills and ability to clarify ill‑defined problems
Ability to communicate effectively with technical peers, non‑technical staff, and executive leadership
Proven ability to build trust across departments with competing priorities

Nice To Haves

Experience with ERP systems in professional services or project‑based organizations
Familiarity with data governance frameworks or formal data quality practices
Experience working in AEC, design, or creative professional environments
Background in internal training, enablement, or change management

Responsibilities

Build, deploy, and support the maintenance and updates of our firm’s custom-built large language model (LLM) for Project Managers, currently in beta testing, as well as the creation of new LLMs for operational teams (e.g., Marketing, Legal, Resource Planning, Finance).
Collaborate on the design and deployment of new AI tools for firm-wide use, ensuring outputs are reliable, interpretable, and aligned to business needs.
Serve as a primary resource for staff using AI productivity tools (e.g., Claude, Cursor, GitHub), answering questions, providing guidance, and developing training and onboarding materials.
Assist in developing prompt engineering standards and AI workflow design guidelines for firm use.
Create tools that Project Managers and Operational Departments use daily.
Maintain existing Power BI dashboards to ensure accuracy, performance, and usability while looking for opportunities to migrating data into our LLM-powered analytics platform, maintaining continuity during the transition.
Build analytics models and data products that serve Projects, Operations, Finance, HR, Marketing, Legal, and Executive Leadership.
Improve data accessibility and trust for project managers, designers, and firm leadership.
Contribute to the design and implementation of a Single Source of Truth (SSOT) architecture using APIs and modern data integration patterns.
Build and maintain data pipelines across structured and unstructured sources, including project data, finance systems, resource planning, BIM data, and operational systems.
Participate in data governance conversations during the firm’s ERP replacement, including requirements gathering, configuration input, and data migration planning and execution.
Define and enforce data quality standards, documentation, lineage, and compliance practices.
Create clear documentation, onboarding materials, and internal communications that support adoption of new tools and workflows.
Support firm‑wide data and AI skill‑building initiatives.
Manage stakeholder expectations through transparent communication as systems and processes evolve.
Contribute to a culture where data quality, experimentation, and thoughtful automation are valued.