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Senior Data Integration Engineer

  • Toronto, ON, Canada

The salary range for this position is $140,000 - $160,000. · Full-time

This opening has closed.

The original listing is kept below for reference.

Job details

Pay
The salary range for this position is $140,000 - $160,000.
Job type
Full-time
Closing date
Posting language
English
Posted

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Original job description

Archived listing. The details below describe a past opening.

Interested in joining one of Canada's top performing asset managers? We are looking for a Senior Data Integration Engineer for an existing vacancy in our IT department, 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.

What You Will Do

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.

What You Bring

  • 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.

The salary range for this position is $140,000 - $160,000. The salary range provided reflects the base salary range for this position as required by legislation. In addition, there is an annual performance bonus which contributes to the total compensation for this position. Further questions may be directed to the HR team during the interview process.

For a closer look at how you can build your career with us, we invite you to explore cclgroup.com. 

CC&L Financial Group is committed to creating a diverse and inclusive environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to gender, ethnicity, religion, sexual orientation or expression, disability, or age. 

Your application will be reviewed by a member of the hiring team - AI is not used in the screening, assessment or selection of applications at this time.

More about this job

Responsibilities

The Senior Data Integration Engineer will design, build, and manage data pipelines and governance controls for enterprise-wide AI and analytics. This role involves ensuring data is optimized for autonomous AI systems and creating a trusted foundation for AI agents and workflows.

Requirements

Candidates should have proven experience in building resilient data pipelines and strong skills in SQL, Python, and orchestration frameworks. Familiarity with cloud platforms and expertise in data modeling and AI concepts are also required.

Skills

  • Data Integration
  • ETL
  • ELT
  • SQL
  • Python
  • Orchestration Frameworks
  • Cloud Platforms
  • Data Modeling
  • Semantic Enrichment
  • Embeddings
  • Vector Databases
  • AI Ethics
  • Security
  • Privacy
  • Compliance
  • Auditability

Visa sponsorship

Not detected in the job text

Categories

  • Technology
  • Data & Analytics
  • Finance & Accounting
  • Software

Keywords

  • Data Integration
  • ETL
  • ELT
  • SQL
  • Python
  • Orchestration Frameworks
  • Cloud Platforms
  • Data Modeling
  • Semantic Enrichment
  • Embeddings
  • Vector Databases
  • AI Ethics
  • Security
  • Privacy
  • Compliance
  • Auditability
  • AI
  • Analytics
  • Governance
  • Autonomous Systems
  • Event-Driven Data Flows
  • Feedback Loops
  • Monitoring Systems
  • Knowledge Sharing
  • Collaboration
  • Business Value
  • Agentic AI
  • Workflows
  • Data Governance
  • Performance Bonus

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