Lead Data Engineer,Associate Director Job Toronto area,Ontario Canada,IT/Tech

Position: Lead Data Engineer, Associate Director )

Fitch Group is currently seeking a Associate Director/Lead Data Engineer based out of our Toronto office.

As a leading, global financial information services provider, Fitch Group delivers vital credit and risk insights, robust data, and dynamic tools to champion more efficient, transparent financial markets. With over 100 years of experience and colleagues in over 30 countries, Fitch Group's culture of credibility, independence, and transparency is embedded throughout its structure, which includes Fitch Ratings, one of the world's top three credit ratings agencies, and Fitch Solutions, a leading provider of insights, data and analytics.

With dual headquarters in London and New York, Fitch Group is owned by Hearst.

Fitch's Technology & Data Team is a dynamic department where innovation meets impact. Our team includes the Chief Data Office, Chief Software Office, Chief Technology Office, Emerging Technology, Shared Technology Services, Technology, Risk and the Executive Program Management Office (EPMO). Driven by our investment in cutting-edge technologies like AI and cloud solutions, we're home to a diverse range of roles and backgrounds united by a shared passion for leveraging modern technology to drive projects that matter to our organization and clients.

We are also proud to be recognized by Built In as a "Best Place to Work in Technology" 3 years in a row. Whether you're an experienced professional or just starting your career, we offer an exciting and supportive environment where you can grow, innovate, and make a difference.

Want to learn more about a career in technology and data at Fitch?

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How You'll Make an Impact:

Lead the design and architecture of end-to-end data pipelines and solutions on modern cloud-based platforms, including Snowflake, Databricks, and AWS.

Build and optimize robust, scalable data orchestration workflows using Apache Airflow and implement best practices across multiple agile squads.

Design and implement data solutions using PostgreSQL for relational data and MongoDB for No

SQL requirements, ensuring optimal performance and scalability.

Architect and deploy containerized data applications using Docker, Kubernetes, and AWS EKS, incorporating Git Hub Actions for automated deployments.

Design and implement CI/CD pipelines using Git Hub Actions, establish branching strategies, and ensure automated testing, code quality checks, and security scanning.

Collaborate with cross-functional teams—including Data Scientists, Analytics teams, and business stakeholders—to translate requirements into scalable technical solutions.

Mentor and guide data engineers by promoting technical excellence, establishing coding standards, and conducting architecture reviews.

Drive data platform modernization initiatives and ensure data quality, reliability, and governance across all data systems.

Design and implement AI-enhanced data pipelines that leverage LLMs and Agentic AI frameworks to automate data quality checks, anomaly detection, and intelligent data transformation workflows.

Architect data infrastructure to support AI/ML workloads, including feature stores, vector databases, and real-time inference pipelines integrated with cloud-native services.

Leverage established standards and best practices to integrate AI agents into data engineering workflows, including context management protocols (MCP) for seamless AI-to-data-platform communication.

You May Be a Good Fit If:

You have 8+ years of data engineering experience, including 3+ years in a lead role architecting large-scale data platforms.

You possess expert-level proficiency in Python and Java for building cloud-native data processing solutions.

You have deep hands-on experience with Apache Airflow, Snowflake (data warehousing, modeling, optimization), and Databricks.

You have strong AWS expertise, including S3, Lambda, Glue, EMR, Kinesis, EKS, and RDS.

You have production database experience with PostgreSQL (design, optimization, replication) and MongoDB (document modeling, sharding, replica sets).

You have solid experience with containerization and orchestration using Docker, Kubernetes, and AWS EKS, including cluster management and autoscaling.

You have proven CI/CD and Git Ops experience using Git Hub, Git Hub Actions, and ArgoCD for automated deployments and multi-environment management.

You are proficient with agile tools such as JIRA for sprint management and Confluence for technical documentation and knowledge sharing.

You have excellent analytical, problem-solving, and communication skills, with the ability to explain complex concepts to non-technical stakeholders and drive initiatives in complex environments.

You have working knowledge of AI/ML frameworks (Lang Chain, Llama Index, Auto Gen, etc.) and understand how Agentic AI can enhance data engineering workflows through automated data validation, intelligent orchestration, and self-healing pipelines.

You have practical understanding of AI integration patterns in data platforms, including prompt…