Senior Data Integration Engineer

Connor, Clark & Lunn Financial Group Ltd.•Toronto, ON

49d•CA$140,000 - CA$160,000•Hybrid

About The Position

Connor, Clark & Lunn Financial Group (CC&LFG) is building an AI Enablement Team to accelerate the responsible adoption of AI across the organization. The Senior Data Integration Engineer will design, build, and manage the pipelines, governance controls, and semantic context that power enterprise-wide AI and analytics. This role goes beyond integration: it ensures data is optimized for autonomous and agentic AI systems that can plan, act, and adapt dynamically within guardrails. At CC&LFG, you will help lay the foundation for agentic AI in financial services — designing the data, governance, and orchestration systems that make AI reliable, explainable, and enterprise-ready. You’ll work on cutting-edge AI pipelines, enabling the business to move from AI-assisted insights to AI-driven autonomous workflows. You will create the trusted foundation on which AI agents, copilots, and orchestration frameworks securely operate. Our offices currently operate on a hybrid model with three days in office.

Requirements

Proven experience building resilient, scalable data pipelines for structured and unstructured datasets.
Strong skills in SQL, Python, orchestration frameworks (Airflow, Prefect, Dagster) and modern data transformation tools.
Familiarity with cloud platforms (Azure, AWS, Microsoft Fabric) and integration with AI/ML services.
Expertise in data modeling, semantic enrichment, embeddings, and vector databases for LLM/RAG pipelines.
Understanding of agentic AI concepts: workflow orchestration, autonomous decision-making, human-in-the-loop design, and safety guardrails.
Knowledge of data governance practices including AI ethics, security, privacy, compliance, and auditability.
Ability to collaborate with engineers, AI product teams, and business leaders to make enterprise data agent-ready.

Responsibilities

Design & Build Pipelines: Develop scalable ETL/ELT processes to integrate structured, semi-structured, and unstructured data from diverse systems (applications, APIs, databases). Enable real-time and event-driven data flows to support autonomous agent decision-making.
Safeguard Data & Governance: Implement robust controls for security, privacy, compliance, and bias mitigation. Establish observability and audit trails for agentic AI pipelines, ensuring transparency and accountability.
Enable AI & Agentic Readiness: Enrich datasets with semantic context and metadata to power LLM ingestion, RAG pipelines, embeddings, and multi-agent collaboration. Build connectors and APIs that allow agentic AI systems to dynamically query, retrieve, and act on enterprise data. Support workflow orchestration across AI agents and business systems.
Operationalize Agentic AI: Develop feedback loops and monitoring systems to detect drift, hallucinations, or risk in autonomous AI actions. Ensure guardrails (policy enforcement, escalation paths, human-in-the-loop design) are integrated into pipelines and workflows.
Streamline Knowledge Sharing: Document integration processes, orchestration patterns, and governance standards to ensure reuse and cross-team adoption. Provide blueprints for how AI agents interact with structured enterprise data.
Collaborate Across Teams: Partner with AI Solutions Engineers, governance leads, and business stakeholders to embed data integration into AI-driven workflows. Drive alignment between technical infrastructure and business value realization from agentic AI.