Data Architect governing World Vision Canada's nonprofit enterprise data architecture. Defining cloud data standards, governance, security and reusable platforms remotely within Canada.
About the role
- Senior Data Engineer building scalable pipelines and AI-ready infrastructure for CINC Systems’ community association management software. Enabling reliable analytics, automation, and intelligent product experiences.
Responsibilities
- Design and build robust, scalable, and secure data pipelines for analytics, APIs, and AI applications
- Architect and maintain modern data infrastructure across cloud environments, with AWS preferred
- Partner with AI and application engineering teams to provide structured, high-quality data for training, inference, and real-time decision systems
- Develop and maintain data models and schemas for analytics and operational use
- Design data contracts and governance patterns supporting data lineage, versioning, and reliability across microservices and AI systems
- Build streaming and event-driven data architectures for low-latency, high-integrity data flows
- Implement data quality automation and observability systems to detect anomalies and validate pipeline health
- Work with Product and Analytics to define KPIs, metrics, and usage data pipelines
- Collaborate with AI engineers on embedding pipelines, RAG data sources, and feature stores
- Improve data engineering practices through code reviews, pairing, and knowledge sharing
- Participate in architecture reviews and contribute to standards for data security, compliance, and scalability
- Use AI-native tools and techniques for data classification, anomaly detection, and metadata enrichment
Requirements
- 8+ years of experience in data engineering or backend software engineering with a strong focus on large-scale data systems
- Advanced proficiency in SQL and one or more programming languages such as Python, TypeScript, or Java
- Experience designing and operating event-driven data architectures and microservices using AWS services (EventBridge, S3, Lambda, API Gateway, DynamoDB, Glue)
- Strong understanding of relational and analytical databases including SQL Server, Postgres, or Redshift
- Experience building and maintaining ETL and ELT pipelines with strong data modeling, versioning, and testing practices
- Familiarity with AI and ML data patterns including embeddings, feature stores, and RAG pipelines
- Knowledge of API-based data access and GraphQL or REST API design principles
- Experience applying DevOps principles to data engineering including CI/CD pipelines, IaC, and observability
- Understanding of data governance, access control, and privacy best practices
- Experience supporting AI and ML applications in production, including integration with APIs like OpenAI, Anthropic, or Bedrock
- Practical understanding of how data quality and architecture affect AI outcomes and product experiences
- Ability to design pipelines that deliver data optimized for model training, fine-tuning, and real-time inference
- Experience working with vector databases such as Weaviate, Pinecone, or Postgres pgvector
- Skilled at identifying opportunities to use automation and AI to improve data engineering workflows
- Hands-on engineering approach and ability to lead by doing
- Excellent communication and ability to bridge engineering, product, and analytics teams
- Ability to diagnose system constraints and simplify complex data flows
- Collaborative mindset, strong ownership, and focus on measurable business impact
- Comfortable mentoring others and setting standards for data craftsmanship across teams
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