Azure Senior Data Lead

Job Summary

Role : AI Tech Lead – Wealth Management

Location : Toronto/Hybrid

Sell Rate : 90 to 100 CAD

Demand ID : We will provide you by tomorrow

Role Summary

The AI Tech Lead is responsible for leading the design, development, and delivery of AI‑driven solutions within Wealth Management, bridging business needs, data platforms, and engineering execution. This role combines hands‑on technical leadership with Wealth Management domain expertise to ensure AI solutions are scalable, secure, compliant, and deliver measurable business value.

Key Responsibilities Technical & AI Leadership

• Provide technical leadership for AI, ML, and GenAI initiatives across Wealth Management platforms.

• Own solution design for AI‑enabled use cases such as client personalization, portfolio insights, intelligent advisory, risk analytics, and operational automation.

• Guide engineering teams on AI architecture, integration patterns, and implementation best practices.

• Ensure AI solutions are production‑ready, performant, and maintainable.

Wealth Management Enablement

• Translate Wealth Management business requirements into technical designs and AI solution blueprints.

• Work closely with Product Owners, Business Analysts, and Architects to align AI capabilities with:

• Investor onboarding & servicing

• Advisory & portfolio management

• Client experience and relationship management

• Embed domain considerations such as suitability, explainability, and transparency into solution design.

Data, Platform & Integration

• Collaborate with Data & Platform teams to ensure high‑quality, governed data pipelines for AI workloads.

• Oversee integration of AI models with core wealth platforms, CRM systems, and digital channels.

• Guide use of cloud platforms and modern data/AI stacks for scalable delivery.

Governance, Risk & Compliance

• Ensure AI solutions align with data privacy, security, regulatory, and responsible AI principles.

• Support explainable AI, auditability, and risk controls critical to Wealth Management environments.

• Participate in architectural and risk reviews with stakeholders.

Delivery & Team Leadership

• Lead sprint‑level technical execution and mentor AI/ML engineers and developers.

• Drive engineering quality, coding standards, and CI/CD discipline.

• Act as a technical point of contact for stakeholders and senior leadership.

Required Skills & Experience

• 8–12+ years of experience in software/data engineering, with strong exposure to AI/ML.

• Hands‑on experience delivering AI/ML or GenAI solutions in enterprise environments.

• Strong Wealth Management domain experience, including investment products, advisory workflows, and client lifecycle.

• Solid understanding of:

• AI/ML fundamentals and GenAI concepts

• Data platforms and analytics pipelines

• Cloud technologies (AWS / Azure / GCP)

• Proven ability to lead teams and drive end‑to‑end technical delivery.

Key Responsibilities

1. Lead the end-to-end technical implementation of data projects using azure data factory, azure databricks, sql, oracle pl/sql, and python.

2. Design and develop efficient and reliable etl processes for large datasets.

3. Collaborate with cross functional teams to understand business requirements and translate them into technical solutions.

4. Optimize data workflows, troubleshoot issues, and ensure data quality and integrity.

5. Implement best practices for data security, governance, and compliance.

6. Provide technical guidance, mentoring, and support to junior team members.

7. Stay uptodate with the latest trends and technologies in data engineering and analytics.

Skill Requirements

1. Proficiency in azure data factory (adf) and azure databricks for data integration and processing.

2. Strong skills in writing complex sql queries for data manipulation and analysis.

3. Experience with oracle pl/sql for database development and management.

4. Proficient in python programming language for automation and scripting tasks.

5. Solid understanding of data warehousing concepts, etl processes, and data modeling.

6. Ability to work in a fast paced environment and manage multiple priorities effectively.

7. Excellent problem-solving skills and attention to detail.

8. Strong communication and interpersonal skills for effective collaboration with team members and stakeholders.

Other Requirements

1.Relevant certifications in Azure Data Factory, Azure Databricks, SQL, Oracle, or Python would be a plus.