Full Stack Lead

Overview

On Site
$140,000 - $160,000
Full Time

Skills

AI/ML concepts
React
Next.js
Node.js
CI/CD pipelines
Python
cloud infrastructure
AWS
GCP
Azure
Airflow
SQL
Angular
GraphQL
cloud-native

Job Details

Title: Full Stack Lead
Location :Seattle, WA OR Vancouver, BC/ ( need to travel once in 3-4 months)
Duration:Fulltime

Mandatory Skills : Airflow and SQL

About the Role
This is a role that blends technical leadership, product thinking, and data-driven innovation. You'll lead a team of engineers in building full stack applications that integrate with , leverage machine learning models, and deliver real-time insights to planners, merchandisers, and supply chain teams.
Key Responsibilities
Lead full stack development using technologies like Angular, React, Node.js, GraphQL, and cloud-native services (AWS/Google Cloud Platform).
Design and build integrations between Anaplan and internal systems to support planning, forecasting, and scenario modeling.
Collaborate with data scientists to operationalize ML models for demand forecasting, inventory optimization, and supply chain analytics.
Develop intuitive tools for planners and business users to interact with forecasts, adjust plans, and simulate outcomes.
Establish and maintain CI/CD pipelines using Gitlab to automate testing, deployment, and monitoring.
Mentor engineers and promote best practices in architecture, testing, and DevOps.
Champion strong engineering practices, including:
o Test-driven development (TDD)
o Code reviews and pair programming
o Scalable architecture and modular design
o Observability, logging, and performance monitoring
Drive agile product development, working closely with Product, UX, and Planning teams to deliver high-impact features.


Qualifications
9+ years of experience in software engineering, with 2+ years in technical leadership role.
Strong full stack development skills with modern frameworks (React, Next.js, Node.js).
Familiarity with AI/ML concepts, especially in time series forecasting, demand sensing, or optimization.
Experience deploying ML models into production environments (e.g., using Sage Maker, Vertex AI, or custom APIs).
Proven experience implementing and managing CI/CD pipelines and automated testing frameworks.
Experience with Python, Pandas, or ML libraries (e.g., Prophet, XGBoost, TensorFlow).
Strong understanding of cloud infrastructure (AWS, Google Cloud Platform, or Azure).
Excellent communication and cross-functional collaboration skills.

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