The Position:
We are looking for an experienced and hands-on Senior Integrations Developer / AI Data Integration Engineer to help lead the evolution of our modern data integration platform. This role combines traditional ETL and data engineering expertise with emerging AI-assisted integration development practices.
You will work on building scalable data ingestion and transformation pipelines while contributing to the development of a next-generation AI-assisted ETL framework that uses agents, metadata, reusable skills, and workflow orchestration to automate portions of the integration lifecycle.
The ideal candidate is equally comfortable reverse-engineering complex client systems, designing robust transformation logic, working directly with data models and business rules, and leveraging AI development tools to accelerate delivery.
This is a foundational role within our growing integrations function and will play a critical role in shaping future integration standards, tooling, and architecture across the organization.
Key Responsibilities:
- Design, build, and maintain scalable ETL/ELT pipelines for onboarding external client data into the ENGIN analytics platform
- Analyze and reverse-engineer client systems, source data structures, and business processes to define integration approaches
- Develop transformation logic, mapping rules, reconciliation processes, and validation workflows for complex utility and enterprise datasets
- Contribute to the design and implementation of AI-assisted and metadata-driven integration development frameworks
- Work with agentic workflows, reusable AI skills, metadata models, and automation pipelines to accelerate integration development
- Develop integration logic using Python, PySpark, SQL, and cloud-native data processing frameworks
- Support deployment and orchestration of ETL workloads within AWS-based infrastructure environments.
- Implement data quality, validation, monitoring, reconciliation, and error-handling processes across integration workflows
- Collaborate with product, delivery, customer, and engineering teams to define data contracts, transformation requirements, and integration standards
- Document integration logic, business rules, workflows, and architectural decisions
- Participate in architectural planning and contribute to long-term modernization of the company's integration ecosystem
- Assist with project estimation, implementation planning, technical discovery, and stakeholder communication
Work location:
We are a hybrid company with offices in Calgary and Toronto. We spend much of our time working remotely but also meet regularly in our offices. Only candidates that can regularly work in either of these locations will be considered.
Skills & Qualifications:
- 5+ years of experience in ETL development, data integration, data engineering, or related roles
- Strong hands-on experience with Python and SQL for data transformation and pipeline development
- Experience working with modern cloud-based data processing and orchestration environments
- Strong understanding of relational data modeling, dimensional modeling, CDC concepts, ODS/data marts, and data warehousing principles
- Experience developing transformation rules, reconciliation logic, mapping logic, and data validation frameworks
- Strong understanding of data profiling, pattern recognition, natural keys vs surrogate keys, and reference/lookup data management
- Experience working with APIs, flat files, databases, and event-driven integration patterns
- Comfortable using AI-assisted development tools to generate, review, and refine technical artifacts and code
- Understanding of modern AI terminology and concepts such as agents, agentic workflows, metadata-driven processing, reusable skills, hooks, and orchestration patterns
- Familiarity with AWS cloud services and distributed data processing concepts
- Strong analytical, troubleshooting, and system discovery skills
- Excellent communication, documentation, and stakeholder collaboration abilities
- Ability to work independently in a fast-paced and evolving technical environment
Nice to Have:
- Experience with PySpark, Apache Spark, EMR, Databricks, or distributed processing frameworks
- Experience with orchestration and workflow tools
- Experience with event-driven architectures and messaging platforms
- Familiarity with DevOps and CI/CD practices for data pipelines
- Exposure to AI/LLM-assisted software development workflows
- Experience mentoring developers or helping establish technical standards and best practices
- Experience working within utility, critical infrastructure, or industrial analytics environments
What success looks like:
- Successfully onboard and modernize complex client data integrations
- Help establish repeatable, scalable integration development standards
- Contribute to the evolution of AI-assisted ETL development capabilities
- Improve integration delivery speed, quality, observability, and maintainability
- Act as a trusted technical contributor across integration, analytics, and platform initiatives
What We Offer:
- Opportunity to work on advanced AI-assisted integration and analytics platforms
- Direct impact on the architecture and future direction of the company's data platform
- High level of ownership, autonomy, and technical influence
- Competitive compensation and benefits
- Collaborative and technically strong engineering culture
- Flexible Hours- Work around a core schedule (10:00 AM - 3:00 PM) while maintaining a work-life balance that suits you.
- Autonomy & Growth- Take ownership of diverse responsibilities, explore cross-disciplinary opportunities, and advance your career in a dynamic, fast-growing company.
