Lead Data Analyst
On-siteToronto, Ontario, Canada
Job Summary
Lead requirements definition and translate complex business needs into precise technical specifications for data contracts, transformation logic, and AI/ML feature requirements. Drive deep-dive analyses on customer behavior and campaign outcomes with a lens toward AI-augmented insight generation. Architect dashboards, scorecards, and executive reporting frameworks while acting as a technical bridge between business stakeholders, engineering, and data science teams. Validate source-to-target mappings, enforce data quality, and ensure AI/ML pipelines consume reliable, well-governed data. Lead production readiness reviews and continuous improvement cycles to ensure solutions are accurate and stable at scale. Mentor junior analysts and engineers while establishing best practices for analytics engineering and AI-ready data design.
Required Qualifications
- 10+ years of progressive experience as a data analyst, analytics engineer, or senior business systems analyst
- Proven ability to lead complex, cross-functional data initiatives from ambiguous requirements through production delivery
- Deep expertise in data mapping, acceptance criteria definition, UAT leadership, and production validation for analytics or data platform solutions
- Expert-level SQL: complex multi-table joins, window functions, query optimization, and performance tuning on large enterprise datasets
- Strong understanding of AI/ML workflows and how data platforms must be designed to support feature engineering, model training pipelines, and real-time inference
- Hands-on knowledge of Kafka, schema registries, and event streaming concepts
- Deep familiarity with modern data platform architectures: data warehouses, Lakehouses (e.g., Delta Lake, Iceberg), and how they serve both BI and AI use cases
- Exceptional stakeholder communication skills: able to translate technical complexity into clear narratives for senior and executive audiences
Desired Qualifications
- Domain experience in financial services — banking, credit data, or regulatory reporting
- Familiarity with LLM/GenAI integration patterns: RAG pipelines, embedding workflows, or AI-assisted analytics
- Experience with GitHub Actions and CI/CD for data pipelines
- Knowledge of Debezium, GraphQL, or ELK Stack (Elasticsearch / Logstash / Kibana)
- Hands-on experience with cloud-native platforms: OpenShift, Kubernetes, S3 object storage
- MongoDB experience: querying semi-structured data, aggregation pipelines for analytics use cases
- Proficiency with BI tools (Tableau, Power BI) and data quality frameworks for trusted, governed reporting
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