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Data Engineer - Job Opportunity at Chelsea Avondale

Toronto, Canada
Full-time
Mid-level
Posted: May 23, 2025
Hybrid
CAD 95,000 - 130,000 per year based on the mid-level experience requirement (3+ years) and the Toronto market. This range reflects the specialized nature of insurance data engineering, the company's position as a cutting-edge InsurTech firm, and the emphasis on Python expertise. The lower end represents solid mid-level compensation while the upper end accounts for candidates with stronger backgrounds in insurance domain knowledge or advanced data engineering capabilities.

Benefits

Flexible hybrid work arrangement with minimal office presence required only once per month, providing exceptional work-life balance and reducing commute costs while maintaining team connectivity
Significant professional development opportunities through exposure to cutting-edge technologies and complex data challenges, with strong organizational support for skill enhancement and career growth
Direct collaboration with top-tier professionals across insurance, software development, finance, and operations, offering unparalleled networking and learning opportunities
High-growth environment with rapidly expanding responsibilities and the ability to directly influence technical direction and business outcomes
Streamlined technology stack focused on core competencies, reducing tool fatigue and allowing deep expertise development in essential technologies
Disability-inclusive workplace with comprehensive accommodations available throughout employment, demonstrating commitment to diversity and inclusion

Key Responsibilities

Architect and implement principled data warehouse solutions that serve as the foundation for company-wide data-driven decision making and directly impact business growth strategies
Lead the design and development of scalable data pipelines handling millions of rows of proprietary insurance data, ensuring high performance and reliability for critical business operations
Transform complex business requirements from Science, Engineering, and Business teams into actionable data products that drive competitive advantage in the insurance market
Pioneer the adoption of innovative data technologies and methodologies while maintaining alignment with the company's streamlined technical philosophy
Drive strategic initiatives for data quality improvement across the organization, establishing best practices and governance frameworks
Create compelling data visualizations and reports that enable self-service analytics for stakeholders at all levels, from technical teams to senior business leaders
Architect robust data ingestion systems supporting advanced ML/AI solutions, handling both structured and unstructured data to power the company's sophisticated risk modeling capabilities
Optimize data models and storage solutions for maximum performance and cost-effectiveness in handling large-scale insurance datasets
Maintain and enhance critical Python-based data infrastructure while ensuring adherence to coding best practices and contributing to technical debt reduction

Requirements

Education

Bachelor's or Master's degree in Mathematics or Statistics

Experience

3+ years of experience in Python and Pandas is key

Required Skills

Python and Pandas Principled data warehouse design Data visualization Data pipeline design and development MongoDB CI/CD tools and methodologies (GitLab, branches, code reviews, merge requests, etc) AWS experience is a plus Great communication skills Experience working independently and driving projects end to end Strong problem solving and analytical skills (performance analysis, defect analysis, and reporting) Organized and meticulous
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Sauge AI Market Intelligence

Industry Trends

The insurance technology sector is experiencing unprecedented growth with InsurTech investments reaching record levels globally. Companies are increasingly leveraging advanced data analytics and machine learning to revolutionize risk assessment and pricing models. The shift towards data-driven decision making in insurance has created significant demand for data engineers who can bridge the gap between traditional actuarial science and modern data technologies. Canadian InsurTech companies are particularly well-positioned as the market matures, with regulatory frameworks becoming more supportive of technological innovation in insurance. The convergence of big data technologies with insurance domain expertise represents a major industry transformation. Traditional insurance companies are being disrupted by tech-forward startups that can process and analyze data at scale to offer more competitive pricing and better risk assessment. This trend is accelerating the need for data engineers who understand both the technical aspects of data infrastructure and the business context of insurance operations. The emphasis on Python as a primary technology stack aligns with industry best practices for data science and engineering in financial services. The insurance industry's adoption of AI and machine learning for risk modeling has created a new category of hybrid roles combining data engineering with domain expertise. Companies are moving away from legacy systems and embracing modern data architectures that can handle real-time processing and complex analytics. The focus on proprietary data and sophisticated modeling indicates a competitive advantage through data capabilities, which is becoming the primary differentiator in the insurance market.

Role Significance

Based on the collaborative nature described and the company structure (including Skynet Software division and Max Insurance), this role likely operates within a data team of 5-10 members, interfacing with larger cross-functional teams across Science, Engineering, and Business units. The emphasis on independent work and end-to-end project ownership suggests a lean team structure where each member has significant individual responsibility rather than working in large, hierarchical teams.
This is a strategic mid-level position with significant influence on the company's data infrastructure and analytical capabilities. While requiring 3+ years of experience, the role carries responsibilities typically associated with more senior positions, including driving company-wide data quality initiatives and influencing technical direction. The direct interaction with senior business leaders and the ability to propose technology improvements indicates a role with considerable autonomy and impact on business outcomes.

Key Projects

Development of real-time risk assessment pipelines processing millions of insurance policies to enable dynamic pricing models that respond to market conditions and individual risk profiles Creation of self-service analytics platforms enabling business stakeholders to generate insights without technical assistance, potentially reducing analysis turnaround time from weeks to hours Implementation of ML/AI data infrastructure supporting predictive models for claim frequency, severity prediction, and fraud detection across the insurance portfolio Design of unified data warehouses consolidating disparate data sources from underwriting, claims, customer service, and external data providers into a single source of truth Development of automated data quality monitoring systems ensuring the integrity of risk models and regulatory compliance reporting

Success Factors

Deep proficiency in Python and Pandas combined with the ability to maintain and enhance complex existing codebases while introducing improvements incrementally. Success requires balancing technical excellence with pragmatic decision-making in a streamlined technology environment. Exceptional communication skills enabling effective collaboration with diverse stakeholders from data scientists to C-suite executives. The ability to translate complex technical concepts into business value propositions is crucial for driving adoption of data solutions. Strong ownership mentality with the capability to work independently on ambiguous problems while maintaining alignment with business objectives. Success requires comfort with uncertainty and the ability to define structure within loosely defined data environments. Mathematical and statistical foundation enabling understanding of insurance risk models and the ability to implement data solutions that preserve model integrity while improving performance and scalability. Adaptability to work within a focused technology stack while still driving innovation through creative problem-solving rather than tool proliferation. Success means maximizing the potential of core technologies rather than constantly seeking new tools.

Market Demand

Very high demand driven by the rapid digitalization of the insurance industry and the scarcity of data engineers with insurance domain knowledge. The combination of mathematical/statistical education requirements with hands-on Python engineering skills creates a relatively small candidate pool. Toronto's growing tech scene and the insurance industry's traditional presence in the city further intensify competition for qualified data engineers.

Important Skills

Critical Skills

Python and Pandas proficiency is absolutely essential as the primary technology stack across the organization. Deep expertise in these tools enables efficient data manipulation, analysis, and pipeline development. The 3+ years requirement indicates need for production-level coding skills beyond basic scripting. Mastery of Pandas for handling large datasets with hundreds of columns is particularly crucial for insurance data analysis. Data warehouse design principles are fundamental for creating scalable, maintainable data infrastructure. Understanding dimensional modeling, slowly changing dimensions, and optimization techniques ensures data solutions can grow with the business. This skill directly impacts the company's ability to leverage its proprietary data for competitive advantage. Communication skills for stakeholder engagement across technical and business teams are non-negotiable. The ability to embed within business teams and present to senior leaders determines the success of data initiatives. This includes written documentation, verbal presentations, and data visualization skills to convey complex insights effectively. Strong analytical and problem-solving capabilities for handling ambiguous data challenges. Insurance data often contains edge cases, missing values, and complex relationships requiring creative solutions. The ability to analyze performance issues and debug data quality problems independently is essential for maintaining system reliability.

Beneficial Skills

AWS cloud experience provides scalability advantages as the company grows. Understanding cloud data services like S3, Redshift, Athena, and EMR enables building cost-effective, elastic data infrastructure. This becomes increasingly important as data volumes grow with business expansion. MongoDB knowledge supports handling unstructured and semi-structured insurance data. Document databases are particularly useful for storing variable policy information and claim documents that don't fit traditional relational schemas. This skill enables more flexible data modeling approaches. Statistical and mathematical background enhances understanding of insurance risk models and actuarial concepts. While not strictly required for data engineering, this knowledge facilitates better collaboration with data scientists and actuaries. It also enables more informed decisions about data structure and quality requirements. DevOps and CI/CD experience with GitLab streamlines deployment and collaboration processes. Understanding infrastructure as code, automated testing, and deployment pipelines increases development velocity. This becomes crucial as the data platform grows in complexity and criticality.

Unique Aspects

Rare combination of cutting-edge technology work within the traditionally conservative insurance industry, offering stability with innovation. The proprietary nature of the data and models provides exposure to unique challenges not found in typical data engineering roles.
Opportunity to influence technical direction in a growing company while working with a focused, streamlined technology stack. This contrasts with many organizations where data engineers must navigate complex legacy systems or overwhelming tool proliferation.
Direct impact on business outcomes through data solutions that drive pricing, risk assessment, and competitive advantage. Unlike pure technology companies, the clear connection between data work and insurance business results provides tangible value creation opportunities.
Access to massive, complex datasets with hundreds of columns and millions of rows that offer unique analytical challenges. The insurance domain provides rich, multi-dimensional data that goes beyond typical e-commerce or social media datasets.
Collaborative environment working with diverse experts from insurance, finance, and technology backgrounds. This interdisciplinary exposure accelerates professional development and builds valuable cross-functional skills.

Career Growth

Typical progression to senior roles within 2-3 years given the high-growth environment and expanding responsibilities mentioned. The company's emphasis on helping team members enhance their skills and take on more responsibilities suggests accelerated career development compared to traditional insurance companies. Fast learners who successfully deliver on key projects could see advancement opportunities within 18 months.

Potential Next Roles

Senior Data Engineer or Lead Data Engineer positions within 18-24 months, taking on larger architectural responsibilities and mentoring junior team members while leading critical data infrastructure projects Data Architecture roles focusing on enterprise-wide data strategy and governance, particularly valuable in the insurance sector where regulatory compliance and data integrity are paramount Machine Learning Engineer positions leveraging the ML/AI infrastructure experience to move closer to model development and deployment, especially in insurance risk modeling and pricing optimization Engineering Manager roles for those interested in people leadership, overseeing data engineering teams and driving technical strategy at the organizational level Principal/Staff Engineer positions as individual contributors driving technical excellence and serving as subject matter experts in insurance data systems

Company Overview

Chelsea Avondale

Chelsea Avondale operates as an innovative InsurTech group combining technology innovation with insurance expertise through its dual structure of Skynet Software (R&D division) and Max Insurance (P&C insurance company). The company positions itself as a disruptor in the traditional insurance market through sophisticated risk modeling and pricing technologies. The emphasis on proprietary data and cutting-edge technology suggests a well-funded startup or scale-up phase company with significant competitive advantages in data and analytics.

As a self-described 'world's most cutting-edge home insurance group,' the company appears to be positioning itself as a technology leader in the Canadian insurance market with global ambitions. The combination of an insurance company with a dedicated software division suggests a vertically integrated approach that provides competitive advantages over traditional insurers relying on third-party technology. The focus on attracting 'brightest minds' indicates strong market positioning to compete for top talent.
Strong presence in the Canadian market with the Toronto office location placing them at the center of Canada's financial services industry. The mention of transforming both Canadian and global insurance landscapes suggests international expansion plans or technology licensing opportunities. Toronto's position as a major tech hub in North America provides access to talent and proximity to both insurance industry partners and technology ecosystem.
High-performance culture emphasizing individual responsibility and ownership, suitable for self-motivated professionals who thrive with autonomy. The streamlined technology approach suggests a pragmatic, results-oriented environment that values depth over breadth in technical skills. The monthly office requirement indicates a flexible, trust-based culture while maintaining team cohesion. The emphasis on continuous learning and rapid growth opportunities reflects a dynamic, fast-paced startup-like environment within the insurance sector.
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