Analytics Engineer for Narvar focused on building data infrastructure and enabling internal analytics. Collaborating with stakeholders to deliver metrics, models, and self - serve analytics solutions.
About the role
- Lead Analytics Engineer leading technical initiatives in a financial technology company focused on AI-ready data insights. Collaborating across teams to enhance the data ecosystem while mentoring engineers.
Responsibilities
- Own the technical architecture and roadmap for our most complex Analytics Engineering initiatives - including semantic layer design, source-of-truth consolidation, and the data foundation for AI and agent-based use cases
- Architect Forward's semantic layer and metrics standards so key business KPIs are defined once, governed clearly, and consumed consistently across dashboards, models, AI agents, and downstream products
- Lead the technical design of the AI-ready data platform - making the modeling, metadata, and governance decisions that make Snowflake Intelligence and other AI/agent capabilities trustworthy, performant, and production-ready
- Drive technical excellence across our dbt project: model architecture, materialization and incremental strategies, performance tuning, macros, testing patterns, and CI/CD practices that scale as data volume and team size grow
- Set and uphold a high bar for craftsmanship across the team - defining standards for SQL style, modeling patterns, documentation, and data quality, and modeling those standards in your own work
- Mentor Senior and Analytics Engineers through hands-on code review, pairing, and design feedback - accelerating their growth into stronger technical contributors
- Partner with the Manager of Analytics Engineering on technical strategy, hiring, and roadmap planning - acting as a deputy for technical decisions and unblocking the team on the hardest problems
- Lead deep technical partnerships with Data Science, Data Engineering, and Core Technology - owning schema migrations, feature deployments, and streaming pipeline contributions where Analytics Engineering is on the critical path
- Evaluate and operationalize high-value third-party data sources and emerging tooling (e.g., Snowflake Cortex, semantic layer frameworks, observability tools) and make recommendations that elevate the platform
- Champion data governance and quality at the platform level - including dbt tests, lineage, cataloging, observability, and compliance with security and regulatory standards - so both stakeholders and AI systems can trust the numbers
Requirements
- 6+ years of experience in Analytics Engineering, Data Engineering, or Business Intelligence, with a track record of leading complex, cross-cutting technical initiatives
- 4+ years of hands-on production experience with dbt, including advanced patterns such as incremental strategies, macros, custom tests, and CI/CD design
- 3+ years of deep experience with a cloud-based data warehouse (Snowflake strongly preferred), including performance tuning and cost optimization
- Expert-level proficiency in SQL and dimensional data modeling, with a portfolio of durable, well-tested models that have served as foundational layers for an organization
- Demonstrated experience designing and operating a semantic layer or metrics layer that serves as an organizational source of truth
- Proven ability to mentor senior engineers, lead architectural decisions, and influence direction across cross-functional teams
- Excellent written and verbal communication skills - able to drive technical alignment with both engineers and non-technical stakeholders.
Benefits
- medical
- dental
- vision
- a flexible time-off policy
- paid parental leave
- RRSP match
- wellness reimbursement
- volunteering days
- annual professional development budget
- charitable donation match
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