Senior Data Engineer

Role Summary

The Senior Data Engineer reports to the Director, Information Technology Operations and designs, builds, and scales the data systems that support analytics, AI-enabled experiences, and organizational decision-making. The role owns significant parts of the data platform, including production pipelines, transformation layers, data models, semantic layers, data quality, observability, and engineering standards. It provides technical direction, mentors engineers and analysts, and partners with technical and business teams to deliver trusted, well-modelled data products. The role also evaluates and implements AI-assisted data engineering capabilities to improve pipeline development, transformation, documentation, metadata, data quality, and governed access to enterprise data while maintaining appropriate engineering controls and review.

Primary Responsibilities:

Data Platform Architecture & Technical Direction

• Responsible for key contribution of significant parts of the data platform and drive long-term architectural decisions related to tooling, data modelling, semantic architecture, automation, and scalability.
• Design data platform capabilities that reliably support analytics, reporting, operational use cases, and AI-enabled experiences.
• Evaluate technical options and establish reusable patterns that improve platform maintainability, extensibility, and engineering effectiveness.

Pipeline Development & Transformation

• Design, build, and maintain scalable, reliable production data pipelines and transformation layers using dbt and a medallion Bronze, Silver, and Gold architecture.
• Write advanced SQL and Python to develop transformations, frameworks, automation, and reusable engineering tooling.
• Responsible for production data pipelines end-to-end, including design, development, testing, deployment, monitoring, troubleshooting, optimization, and ongoing support.

Data Modelling & Semantic Architecture

• Lead data modelling across dimensional models, slowly changing dimensions, fact and dimension design, and well-structured Silver and Gold layers.
• Design and evolve semantic layers that define business entities, metrics, relationships, calculations, and governed business logic for consistent use across BI, analytics, and AI applications.
• Develop metadata-driven and governed data structures that make enterprise data discoverable, understandable, and consistently consumable.

AI-Assisted Data Engineering & Enablement

• Evaluate and apply AI-assisted engineering approaches to accelerate pipeline design, transformation logic, SQL generation, testing, documentation, troubleshooting, and optimization while maintaining strong review and engineering controls.
• Explore AI-assisted schema understanding, metadata generation, relationship discovery, business-concept mapping, and generation of semantic definitions.
• Partner with Analytics and Innovation AI Labs to enable natural-language-to-data, intelligent analytics, semantic retrieval, and other AI or LLM-based uses of governed enterprise data.

Data Quality & Observability

• Responsible for evolving key elements of the data quality strategy, including testing frameworks, monitoring, alerting, service levels, observability, anomaly detection, and platform-wide validation.
• Implement controls that identify pipeline, transformation, model, metadata, and semantic-layer issues early and support timely root-cause analysis and remediation.
• Promote trusted data products through clear quality standards, transparent monitoring, and continuous improvement of reliability and usability.

Engineering Practices & Delivery Standards

• Establish and champion engineering practices covering version control, CI/CD, code review, documentation, automated testing, clean maintainable code, and dbt project standards.
• Develop reusable frameworks and automation that improve delivery consistency, reduce manual effort, and support safe changes across data environments.
• Contribute to governance, cataloguing, lineage, metadata management, and data-contract practices that strengthen understanding and control of enterprise data.

Partnership, Mentorship & Team Growth

• Collaborate with analysts, data scientists, product teams, and business stakeholders to translate business requirements into trusted, well-modelled data products.
• Mentor and support engineers and analysts, provide technical guidance, and contribute to team capability and engineering maturity.
• Communicate architecture decisions, data definitions, quality expectations, risks, and technical trade-offs clearly to technical and business stakeholders.
• Contribute to the culture of the company, fostering Exchange Solutions' core values and policies.

Capability Requirements - Education, Skills & Experience:

Education & Experience

• Post secondary education, with a degree in Data Engineering.
• A minimum of 5 years’ progressive data engineering experience, with a demonstrated record of owning production data pipelines end-to-end.
• Experience designing, developing, and operating data platforms that support analytics, reporting, operational use cases, or AI-enabled applications.
• Experience translating business and analytical requirements into trusted data products in collaboration with technical and business stakeholders.
• Experience leading data engineering projects or mentoring engineers and analysts is an asset.
• Experience in a loyalty, retail, financial services, or other data-intensive environment is an asset.

Technical Skills

• Expert SQL skills and strong Python proficiency for data engineering; working knowledge of Jinja.
• Deep hands-on dbt experience, including advanced modelling, macros, packages, testing, documentation, and project architecture using dbt Core or dbt Cloud.
• Strong command of medallion architecture and data modelling, including dimensional modelling, slowly changing dimensions, fact and dimension design, and semantic layers.
• Experience designing or working with semantic models, governed business metrics, metadata-driven architectures, or governed business definitions.
• Experience with cloud data warehouses such as Snowflake, BigQuery, Databricks, or Redshift and with cloud platforms such as AWS, Azure, or GCP.
• Experience building and operating data quality, testing, monitoring, alerting, lineage, cataloguing, or observability capabilities.
• Solid software engineering practices covering Git, CI/CD, automated testing, code review, documentation, and clean maintainable code.
• Experience with orchestration tools such as Airflow, Dagster, Prefect, dbt Cloud, or Snowflake Tasks is an asset.
• Familiarity with streaming or real-time data and infrastructure-as-code tools such as Terraform is an asset.
• Ability to evaluate and safely incorporate AI-assisted tools into data and software engineering workflows, including development assistance, metadata enrichment, automated documentation, and intelligent data-quality tooling.
• Experience with AI or LLM-enabled data platforms, natural-language-to-SQL, semantic retrieval, knowledge graphs, ontologies, semantic views, or agentic data engineering workflows is an asset.

Competencies & Behaviours

• Ownership & Accountability - Takes end-to-end responsibility for production data pipelines, platform components, engineering quality, and resolution of risks or failures.
• Architecture & Technical Judgement - Makes practical, forward-looking decisions that balance scalability, reliability, maintainability, data quality, delivery needs, and long-term platform direction.
• Communication - Translates business requirements and complex data concepts into clear models, technical decisions, documentation, and guidance for technical and non-technical stakeholders.
• Collaboration & Mentorship - Builds effective relationships across engineering, analytics, data science, Product, and business teams and raises team capability through practical coaching and knowledge sharing.
• Innovation & Responsible AI - Explores new tools and AI-assisted approaches thoughtfully, validates their value, and applies appropriate review, governance, quality, and security controls.
• Attention to Detail - Applies a disciplined approach to modelling, transformation logic, testing, metadata, semantic definitions, documentation, and production changes.