Position Summary
Modular Solutions is seeking a Director of Data & Analytics to own the architecture and governance of our data and analytics platform and how our data is turned into decisions, so that the data we collect becomes reporting and answers that clients and teams can trust as we continue to grow our platform, client base, and internal capabilities.
Reporting to the CTO, this role leads the Data function, including data engineering, data science, business intelligence, analytics, and migration, and owns the architecture of the data and analytics platform that connects them. While the data and analytics platform is the primary focus, the role also contributes to and collaborates on the data architecture and design of our core application alongside our Product and Development teams.
The ideal candidate is a hands-on leader who sets direction and stays close to the work: designing the analytics data architecture, data models, and semantic layer alongside engineers rather than managing from a distance. You should bring deep experience in data architecture, data modelling, BI and analytics, and data governance, with a working knowledge of data migration and AI-enabled workflows, along with the ability to lead people, prioritize work, and translate complex technical concepts into clear business outcomes.
This role is well suited for someone who can build structure, develop teams, improve processes, and help Modular responsibly adopt data and AI practices that create real value for clients, employees, and the platform.
Key Responsibilities
- Set the data strategy and long-term roadmap for the Data function, including platforms, vendors, budget, and cost, and build a high-performing team across data engineering, BI and analytics, and data migration.
- Own the data and analytics platform architecture: the target-state design for how data flows out of our application into ingestion and pipelines, the data warehouse and data marts, the semantic layer, and the BI and AI tools that consume them, supported by reference architectures and documented architecture decisions.
- Define and maintain the analytics data model for insurance data, including policy, billing, claims, bordereaux, brokers, and coverage, aligned with the application's data model, so that migration, reporting, and analytics map to it consistently.
- Collaborate with Product and Development on the data architecture and design of our core application: bring a data perspective to schema and data model design, assess the downstream impact of changes, favour incremental improvements over large-scale redesign, and establish data contracts so that application changes do not break reporting, integrations, or client deliverables.
- Own metric governance: a single set of business definitions and KPIs with clear owners, implemented once in the semantic layer and consumed consistently by BI, client reporting, and AI.
- Design how clients access their reporting and analytics data, including governed data sharing and extracts, partnering with product engineering where access runs through application APIs, with appropriate tenant isolation, security, and performance.
- Set standards for data quality, governance, privacy, security, and data residency, including PII classification, retention, lineage, and access control, in support of our security and compliance programs.
- Establish a data quality and validation framework, including automated reconciliation, testing, and certification of trusted datasets and AI-generated outputs, so that our teams and clients can trust results at scale.
- Define the reporting operating model for standard and client-specific reporting, including intake and prioritization, move clients toward governed self-service analytics, and deliver executive-level insights and advanced analytics, such as forecasting, where they create clear business value.
- Build data migration and onboarding into a repeatable capability, with a reusable conversion framework, reconciliation, and post-migration validation, led by a dedicated migration lead.
- Build AI-ready data foundations so that AI features call governed data and semantic models rather than calculating on their own, lead AI adoption within the Data team, and help define the product direction for self-service and AI-assisted analytics.
- Partner with Product, Development, Implementation, Operations, and leadership to align priorities, and communicate progress, risks, and trade-offs clearly to executives.
- Perform other duties as required to support the Data function.
Required Qualifications
- 8+ years of experience in data architecture, data engineering, business intelligence, analytics, or a related data field, including designing data platforms or data models at an organizational level.
- 3+ years of experience leading, mentoring, or managing technical teams, while remaining hands-on in the work.
- Proven experience designing enterprise data architecture, including data models, data warehouses and marts, ETL/ELT pipelines, semantic models, and the integration between operational systems and analytics platforms, with a track record of collaborating with application engineering teams on data design and improving existing systems incrementally.
- Hands-on experience building and governing business definitions, metrics, or semantic models, and working directly with engineers to implement them.
- Strong grounding in data governance, data quality, privacy, and security, including access control, PII handling, lineage, and validation of data and reporting outputs.
- Experience leading data migration, data conversion, or reconciliation initiatives.
- Strong technical knowledge of SQL, relational databases, and cloud data platforms, with experience in tools such as Snowflake, PostgreSQL, Power BI, Microsoft Fabric, Azure, and Python, or similar technologies. Familiarity with C# or TypeScript is an asset.
- Hands-on experience using AI tools in day-to-day work, and the ability to encourage and support their use across a team, with sound judgment on accuracy, privacy, and security.
- Experience designing KPI frameworks and delivering executive-level reporting, with strong data visualization and storytelling skills.
- Demonstrated ability to translate business needs into technical direction and technical concepts into clear business language, and to set standards and repeatable operating models across technical and non-technical teams.
- Strong leadership, communication, and problem-solving skills, with the ability to manage ambiguity, competing priorities, and cross-functional dependencies.
Preferred Qualifications
- Background in the insurance, InsurTech, SaaS, financial services, or other complex regulated environments.
- Familiarity with insurance data, including policy, billing, claims, broker, customer, coverage, risk, transaction, or reporting data.
- Previous involvement in AI adoption, AI enablement, or AI transformation initiatives within a software, data, or product organization.
- Hands-on exposure to AI-powered internal tools, workflow automation, document analysis, reporting automation, data validation, or agentic systems.
- Experience with multi-tenant SaaS data architectures, client data sharing, or data residency requirements.
- Proven ability to partner with Product, Development, Implementation, and executive leadership teams to align data and AI initiatives with business priorities.
- Familiarity with Agile delivery tools and practices, including Azure DevOps, Jira, Scrum, Kanban, or similar methodologies.
- Ability to scale teams, hire technical talent, and support career growth for data and AI professionals.
What Success Looks Like in Your First Year
- A documented target-state analytics data architecture and data model, with a roadmap agreed with business and engineering leadership.
- A governed semantic layer in production for our released data marts, serving as the single source of business definitions for reporting and AI.
- Automated validation in place for key client reporting, including bordereaux, replacing manual checking.
- A repeatable data migration framework, with a dedicated migration lead in place.
- A clear reporting operating model and a plan for client self-service analytics.
Our Technology Stack and Architecture:
Our core platform uses C#/.NET, Entity Framework, PostgreSQL, React, TypeScript, and Azure, with Snowflake as our data warehouse. This role partners with product engineering teams on system logic, database structures, APIs, and platform requirements related to data architecture, migration, BI, the semantic layer, data quality, and automation workflows.
What We Offer
- Be part of a growing company building a modern, enterprise-grade platform for the insurance industry.
- Work alongside experienced insurance and technology professionals in a collaborative environment.
- Flexible remote work environment, with a preference for Alberta-based team members, and periodic in-person planning sessions and team events.
- Extended health, dental, and vision benefits.
- Access to an Employee and Family Assistance Program.
- Annual professional development allowance.
- Additional paid time off beyond standard statutory holidays and vacation.
- Recognition for meaningful contributions and career milestones.
- Share Appreciation Rights program so you participate in the company's long-term success.
To Apply:
Please submit your resume and a brief cover letter outlining your relevant experience and what excites you about this opportunity. We thank all applicants for their interest; only those selected for an interview will be contacted.
