Manager, Analytics Engineering

FGL Sports; Toronto; On-site; CAD 120,000 - 180,000; Full time

Job summary

FGL Sports is seeking a hands-on, strategic Manager, Analytics Engineering to lead data-platform modernization from Hadoop to Microsoft Azure. You will build AI-enabled migration frameworks, ensure data quality, and drive scalable, secure pipelines across Azure.

You will lead teams across IT, architecture, governance, privacy, and security, embedding controls and generating measurable improvements in delivery speed, reliability, and cost efficiency.

Qualifications

• Progressive experience in data engineering, data-platform modernization, or cloud migration.
• Demonstrated experience migrating Hadoop-based data and workloads to Microsoft Azure.
• Strong knowledge of distributed data processing, data pipelines, orchestration, data modelling, storage patterns, and production operations.
• Experience with governance, privacy, security, access controls, metadata, lineage, retention, and audit requirements.

Responsibilities

• Lead the end-to-end Hadoop-to-Azure migration, including waves, dependencies, acceptance criteria, cutover plans and rollback strategies.
• Design and deliver secure, scalable batch and streaming data pipelines on Azure with reusable AI-enabled frameworks.
• Develop AI-assisted optimization frameworks to identify bottlenecks and automate tuning tasks while validating outcomes.
• Provide people leadership, translate strategy into roadmaps, and manage delivery across teams and partners.

Skills

Data engineering

Data-platform modernization

Cloud migration

Leadership

Education

Bachelor's degree in computer science, engineering

Tools

Azure Data Services

Job description

What you'll do :

We are seeking a hands-on, strategic Manager, Analytics Engineering to lead the modernization of enterprise data platforms from Hadoop to Microsoft Azure. A core mandate is to build reusable, AI-enabled frameworks that make migration, data engineering, and workload optimization as efficient and accurate as possible. This role will combine intelligent automation with rigorous validation to improve delivery speed, data quality, reliability, scalability, and cost efficiency. The successful candidate will combine technical depth with strong people leadership and program execution. They will partner with architecture, analytics, security, infrastructure, and business teams to establish AI-enabled engineering and migration patterns, manage delivery risks, and build a sustainable Azure data platform. Frameworks must include measurable quality controls, traceability, and human review where needed to validate AI-generated outputs and manage exceptions.

Key Responsibilities

Hadoop-to-Azure Migration

• Lead the end-to-end migration of data, pipelines, and workloads from Hadoop environments to Azure.
• Define migration waves, priorities, dependencies, acceptance criteria, cutover plans, and rollback strategies.
• Assess existing Hadoop jobs, datasets, integrations, and technical debt to determine whether workloads should be rehosted, refactored, rebuilt, retired, or consolidated.
• Build reusable, AI-enabled migration frameworks to accelerate workload assessment, code conversion, test generation, and documentation, with automated reconciliation and human approval for production cutovers.
• Coordinate data validation, parallel runs, production cutovers, decommissioning, and post-migration stabilization.

Data Engineering

• Lead the design and delivery of secure, scalable, and maintainable batch and streaming data pipelines on Azure.
• Build and maintain reusable, AI-enabled data engineering frameworks for pipeline development, code generation, automated testing, data-quality checks, and documentation; establish standards for source control, CI/CD, observability, and lineage.
• Guide teams in designing reliable data models and curated data products that support analytics, reporting, and advanced use cases.
• Partner with IT, architecture, governance, privacy, and security teams to embed controls throughout the data lifecycle, including approved AI tools, protection of sensitive data, traceability of AI-generated changes, and risk-based human review.

Workload and Job Optimization

• Develop AI-assisted optimization frameworks to identify bottlenecks, recommend improvements, and automate repeatable tuning tasks; validate recommendations against runtime, throughput, accuracy, stability, resource utilization, and cloud-cost baselines.
• Identify inefficient queries, data skew, partitioning issues, orchestration bottlenecks, unnecessary data movement, and failed-job patterns.
• Establish performance baselines, service-level objectives, monitoring, alerting, capacity plans, and cost-management practices; measure framework effectiveness through delivery time, manual effort, data accuracy, defect rates, and processing cost.
• Prioritize optimization opportunities using measurable business value, risk reduction, and total cost of ownership.

People Leadership and Delivery

• Lead, coach, and develop data engineers and technical leads, building capability in responsible AI-assisted engineering and driving adoption of reusable frameworks, accountability, collaboration, and continuous improvement.
• Translate the modernization strategy into clear roadmaps, delivery plans, resource needs, and team objectives.
• Manage delivery across internal teams and external partners, proactively addressing scope, schedule, quality, risk, and dependencies.
• Communicate technical decisions, migration progress, trade-offs, and outcomes to both technical and executive audiences.

What you bring:-

• Progressive experience in data engineering, data-platform modernization, or cloud migration, including experience leading technical teams.
• Demonstrated experience migrating Hadoop-based data and workloads to Microsoft Azure.
• Strong knowledge of distributed data processing, data pipelines, orchestration, data modelling, storage patterns, and production operations.
• Experience with relevant Azure data services and modern engineering practices, including building reusable AI-enabled frameworks for migration, pipeline development, testing, and optimization, with demonstrated ability to validate outputs and measure efficiency and accuracy gains.
• Proven ability to tune data jobs, troubleshoot complex performance issues, and improve reliability and cloud cost efficiency.
• Working knowledge of data governance, security, privacy, access controls, metadata, lineage, retention, and audit requirements.
• Strong stakeholder management, communication, prioritization, and change-leadership skills.
• Bachelor's degree in computer science, engineering