Senior Manager of Data Engineering and Analytics

The New Network; Canada; On-site; CAD 110,000 - 140,000; Full time

Job summary

A data analytics company in Canada is seeking a Senior Manager, Data to lead the evolution of their data platform. This role involves mentoring a high-impact team and implementing effective data governance while collaborating with cross-functional teams. The ideal candidate will have over 5 years of experience in data engineering and analytics, and a history of building and managing successful data systems. A proven track record with tools such as Snowflake and Looker is preferred.

Qualifications

• 5+ years in data engineering and analytics with 3+ years in management.
• Experience in defining data strategy and technical solutions.
• Proficient in data lifecycle processes and infrastructure integration.

Responsibilities

• Set vision for data supporting growth and infrastructure.
• Establish reliable, secure, and accessible data systems.
• Coach and grow a high-impact data team.

Skills

Leadership

Data engineering

Analytics

Cross-functional collaboration

ETL/ELT pipelines

Business intelligence

Tools

Snowflake

dbt

Fivetran

Airflow

Looker

Job description

We are looking for a Senior Manager, Data (Engineering & Analytics) to own and evolve our clients data platform, shaping how they transform raw information into trusted, actionable insights. In this role, you’ll be a true player-coach: leading a high-impact team while rolling up your sleeves to build a reliable, scalable data ecosystem.

You will partner closely with Go-to-Market (GTM), Product, and Finance leaders to ensure analytics drive real business impact—not just pretty dashboards. Your work will directly increase data trust across the organization, reduce noise, and empower every team to move fast and confidently.

The Impact You’ll Make

• Vision & Strategy: Set a pragmatic vision for how data supports our rapid growth, defining the fundamentals of infrastructure, modeling, and analytics as we scale.
• Foundation Building: Establish the core data foundations from the ground up, creating reliable, secure, and accessible systems.
• Pragmatic Governance: Implement simple, effective data governance that clarifies ownership and standards without slowing the team down.
• Mentorship: Coach and grow a high-impact team, setting high expectations and working alongside them to solve complex problems.
• Cross-Functional Partnership: Align data efforts with revenue visibility, operational efficiency, and executive decision-making.

What you bring

Must-Haves

• Experience: At least 5+ years in data engineering and analytics, with 3+ years of direct people management experience.
• Leadership Style: A proven "player-coach" mentality—you are equally comfortable defining a high-level data strategy and jumping into the codebase to fix a pipeline.
• Full-Stack Technical Depth: Deep expertise across the entire data lifecycle, including ETL/ELT pipelines, data warehousing, orchestration, and business intelligence.
• Strategic Metric Definition: Demonstrated experience building KPI frameworks and "North Star" metrics that align technical work with business outcomes.
• Systems Architecture: High proficiency in designing infrastructure that integrates disparate systems (e.g., CRM, CDP, and Data Warehouses) into a single source of truth.
• Change Management: Experience guiding cross-functional teams through shifts in data governance, tooling, and process.

Nice-to-Haves

• FinTech/SaaS Experience: Previous experience working within financial services, Earned Wage Access (EWA), or high-growth B2B2C startups.
• Modern Data Stack (MDS) Proficiency: Experience with specific tools like Snowflake, dbt, Fivetran, Airflow, or Looker.
• Scaling Success: A track record of scaling a data department from its foundational stages to a multi-person, high-performing team.
• Governance Expertise: Familiarity with implementing automated data quality checks and maintaining compliance standards (e.g., SOC2, GDPR).
• Advanced Analytics: Experience with predictive modelling or machine learning applications that drive operational efficiency.