Engineer II – Senior Data Engineer (Azure Databricks / Data Engineering)

Work Location Toronto, Ontario, Canada Hours 37.5 Line Of Business Technology Solutions Pay Details $96,900 - $136,800 CAD. This role is temporarily eligible for a pay premium above the posted salary range that is reassessed annually. You are encouraged to discuss specific pay details with your recruiter.

Job Description Develops and maintains technical solutions that adhere to engineering and architectural design principles while meeting business requirements. Provides deep technical expertise in data engineering with a focus on Azure Databricks, Python notebook development, job orchestration, API service layers, and scalable data platforms across ADLS, ADF, Azure Databricks (including Unity Catalog and Lakeflow), Azure SQL, Airflow, and SQL. Designs, builds, operationalizes, and supports reliable, secure, and high‑performing enterprise‑grade data solutions that can scale to process multi‑million records daily in compliance with enterprise and industry standards.

Key Accountabilities Customer Leverage deep technology expertise in data engineering, Azure Databricks, Python, SQL, and cloud data platforms to deliver and ensure that all areas across the organization that provision, manage and support various technologies have the necessary tools, processes and documentation required to effectively execute on their respective mandates. Execute on Engineering strategy as it relates to the introduction of tools and the automation of data ingestion, transformation, notebook execution, job scheduling, and deployment activities across Azure Databricks, ADF, Airflow, API services, and Azure-based data platforms. Partner with the Operations team to automatically integrate with appropriate tools and processes as part of automated/self‑serve data pipeline, notebook job, and API service releases.

Work with partners across Technology and apply in‑depth understanding of relevant business data needs to identify and leverage synergies across the various areas. Act as the expert or lead innovator and agent of change for the programs and services under management, driving modern data engineering practices, reusable frameworks, and platform standards. Work with other teams to implement best practices for engineering and management, including enterprise‑grade software development, reusable engineering frameworks, and AI integration into the SDLC through reusable prompts, skills, instructions, and agents.

Work with vendor platform providers and engineering peers to keep abreast of trends, products, frameworks, and applications. Identify and effectively manage stakeholder engagement and impacts across the enterprise. Interpret client needs, assess engineering related requirements and identify solutions to non‑standard requests.

Shareholder Apply best practices and knowledge of internal / external business issues to improve products or services in own discipline. Monitor and control costs within own work. May interact with governance and control groups, e.g. regulatory / operational risk, compliance and audit to provide subject matter expertise and consult on risk issues related to Engineering technology and tools.

May develop and/or contribute to negotiations of third party contracts/agreements. Maintain knowledge and understanding of external development, engineering and emerging solutions, market conditions and their impact, with particular focus on cloud data engineering, Databricks capabilities, API enablement, and scalable data processing patterns. Proactively identify emerging technologies and innovative solutions for building more robust platform domains, including data platforms capable of handling multi‑million records daily with strong reliability, governance, and performance.

Employee / Team Continuously enhance knowledge/expertise in own area and keep current with emerging industry trends, new technologies and best practices in the external market that can contribute to delivering effective client solutions. Prioritize and manage own workload in order to deliver quality results and meet timelines. Support a positive work environment that promotes service to the business, quality, innovation and teamwork and ensure timely communication of issues/ points of interest.

Participate in knowledge transfer with senior management, the team, other technical areas and business units. Work effectively as a team, supporting other members of the team in achieving business objectives and providing client services. Identify and recommend opportunities to enhance productivity, effectiveness and operational efficiency of the business unit and/or team through reusable data engineering components, automated orchestration, AI‑assisted SDLC practices, and scalable processing frameworks.

Breadth & Depth Expert knowledge of specific domain or range of engineering frameworks, technology, tools, processes and procedures, with specialization in Azure Databricks, Python notebook development, job orchestration, SQL, API service layers, ADLS, ADF, Azure SQL, Airflow, Unity Catalog, and Lakeflow. Expert knowledge of TD applications, systems, networks, innovation, design activities, best practices, business / organization, Bank standards, and may fulfill a governance role across enterprise data ecosystems, data governance, and scalable cloud platform implementations. Expert knowledge and experience in own discipline as a senior data engineer building enterprise‑grade software, reusable frameworks, and robust data services; integrates knowledge of business and functional priorities.

Acts as a key contributor in a complex and critical environment delivering scalable data solutions that support processing of multi‑million records daily. May provide leadership to teams or projects; shares expertise. Applies in‑depth skills and broad knowledge of the business to address complex problems and non‑standard situations involving large‑scale data ingestion, transformation, orchestration, API enablement, and governed analytics platforms.

Generally reports to a Senior Manager or above. Experience & Education University or post‑graduate degree. Strong academic background (e.g., computer science, engineering). 7+ years relevant experience in data engineering, including Azure Databricks, Python notebooks, job orchestration, SQL development, API service layer implementation, and hands‑on experience with ADLS, ADF, Azure Databricks (with Unity Catalog and Lakeflow), Azure SQL, and Airflow.

Nice to have: experience integrating AI into the SDLC using reusable prompts, skills, instructions, and agents; building enterprise‑grade software and frameworks; and designing scalable solutions capable of handling multi‑million records daily. Our Total Rewards Package The package includes base salary, variable compensation, and several key plans such as health and well‑being benefits, savings and retirement programs, paid time off, banking benefits and discounts, career development, and reward and recognition programs. #J-18808-Ljbffr