Job description

Tricon is an owner, operator and developer of single-family rental homes in the U.S. and multi-family apartments in Canada. Our commitment to enriching the lives of our employees, residents and local communities underpins Tricon’s culture and business philosophy. We provide high-quality rental housing options for families across the United States and Canada through our technology-enabled operating platform and dedicated on-the-ground operating teams. Our development programs are also delivering thousands of new rental homes and apartments as part of our commitment to help solve the housing supply shortage. Job Description The Data Architect owns the foundational design of the data platform, from how raw data enters the warehouse to how it surfaces as trusted metrics in business tools, covering the full stack: ingestion design, bronze-to-gold medallion layer structure, transformation standards, orchestration patterns, and the semantic layer that business users interact with. The Data Architect is responsible for platform health - reliability, cost, performance, and broader platform design. They work alongside the North American and Indian Operations engineering teams as the primary technical authority, setting direction, reviewing work, and escalating complex problems. The Data Architect also works with the governance function to make sure the platform is traceable and controlled, and with the enterprise program management office to scope requirements when major initiatives have a data component. Essential Duties and Responsibilities include the following but are not limited to the job specifications contained herein. Additional duties or job functions that can be performed safely may be required as deemed necessary by supervisory personnel. Own the architecture of the data platform end-to-end: warehouse design, medallion layer conventions (bronze, silver, gold), transformation standards, orchestration patterns, and semantic layer governance. Define what each medallion layer means in practice: what lands in bronze as raw source data, what gets cleaned and conformed in silver, what gets shaped into business-ready models in gold, and hold those boundaries consistently as the platform evolves. Own platform health: track and manage compute costs, query performance, pipeline reliability, and model freshness. Identify degradation before it reaches business users. Set ingestion standards for new data sources: landing zone design, schema evolution handling, change detection patterns, and failure recovery approaches. Serve as the technical escalation point for the India Operations engineering team. Conduct architecture reviews, provide design guidance, and review code for pattern compliance. Maintain platform documentation: architecture decision records, data flow diagrams, runbooks, and post-incident reviews. Evaluate new tools and approaches and own the technical roadmap for platform infrastructure. Partner with Data Governance on quality enforcement, lineage tracking, and access control design across the warehouse and downstream tools. Define and maintain the semantic layer, a single authoritative definition of business metrics that any report or analyst can rely on. Partner with analytics and BI teams to validate the semantic layer reflects how the business asks questions and define row-level security and access patterns that control metric exposure by role. Get involved early in cross-functional projects to help shape data requirements and platform needs before implementation begins, rather than inheriting decisions after the fact. Sit on the Technical Review Board, evaluating major architectural decisions and engineering practices for consistency with platform standards before they are implemented. Define and maintain enterprise data standards - naming conventions, documentation requirements, and data quality benchmarks - that apply consistently across the platform. Own the role-based access control (RBAC) design across the warehouse, defining role hierarchies, privilege tiers, and access patterns that balance self-serve analytics with data security requirements. This includes functional roles, object-level grants, and a governance process for access requests. Design and maintain a partitioned virtual warehouse strategy, right-sizing compute clusters for distinct workload types (pipeline execution, ad-hoc analytics, BI tool queries) to control cost and prevent resource contention between workloads. Own the environment architecture: dev, test, and production hierarchy, including promotion workflows and the guardrails that prevent lower-environment changes from reaching production without review. Define storage optimization patterns: clustering keys, table design decisions, and partition strategies that keep query performance predictable as data volumes grow. Govern data masking policies, dynamic data access controls, and network-level security configurations to meet internal security standards and external compliance requirements. Qualifications: Demonstrated experience with architecture design fundamentals: schema structure, database and environment hierarchy (dev, test, prod), and the maintenance practices (object naming conventions, deprecated object cleanup, environment drift prevention) that keep a warehouse organized as it scales. Deep experience designing cloud data warehouse schemas and medallion architectures. You have built and maintained bronze, silver, and gold layers in a production environment. Strong transformation skills: you know how to structure a transformation project for long-term maintainability, not just correctness on day one. Hands-on experience with cloud orchestration platforms and the failure modes that matter in production: retry logic, alerting, dependency management, and error handling. Experience designing and governing a semantic layer, centralizing metric definitions, managing change, and keeping business logic out of individual reports. Comfortable setting standards and getting consistent adoption without becoming a bottleneck on every decision. Can explain architecture decisions to non-technical stakeholders without losing the nuance that makes the decision defensible. Treats compute cost and reliability as real deliverables with the same weight as feature work. Minimum Requirements: 7 years in data engineering or analytics engineering, with at least 3 years in a senior technical or architecture role. Preferred Attributes: Experience in SFR, multifamily, or real estate data environments. Familiarity with operational real estate data is an advantage. Familiarity with property management platforms as data sources. Relevant data platform certifications (cloud warehouse, transformation tooling, or equivalent). Experience with Snowflake, dbt, Airflow, Purview, Power BI, and Sigma. Prior experience in an institutional investor or private equity portfolio company. Physical Demands: The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job, including: Frequently required to sit, talk, and hear. Frequently required to stand and move; use hands to grasp, squeeze, finger, handle, and feel; reach, push, and pull with hands and arms; occasionally required to twist; reach overhead; stoop, kneel, squat, bend, and crouch. Occasionally lift, carry, and move up to 10 pounds. Vision abilities required by this job include close vision, distance vision, and depth perception. At Tricon, we are committed to creating a workplace where every individual is valued for their unique contributions, experiences, voices, and backgrounds. By embracing these principles, we aim to positively impact our business and the communities we serve, creating a lasting legacy where everyone can thrive. #TriconHomes

Requirements

Must-haves

Own the architecture of the data platform end-to-end: warehouse design, medallion layer conventions (bronze, silver, gold), transformation standards, orchestration patterns, and semantic layer governance.
Own platform health: track and manage compute costs, query performance, pipeline reliability, and model freshness. Identify degradation before it reaches business users.
Set ingestion standards for new data sources: landing zone design, schema evolution handling, change detection patterns, and failure recovery approaches.
Serve as the technical escalation point for the India Operations engineering team. Conduct architecture reviews, provide design guidance, and review code for pattern compliance.
Maintain platform documentation: architecture decision records, data flow diagrams, runbooks, and post-incident reviews.
Evaluate new tools and approaches and own the technical roadmap for platform infrastructure.
Partner with Data Governance on quality enforcement, lineage tracking, and access control design across the warehouse and downstream tools.
Define and maintain the semantic layer, a single authoritative definition of business metrics that any report or analyst can rely on.
Partner with analytics and BI teams to validate the semantic layer reflects how the business asks questions and define row-level security and access patterns that control metric exposure by role.
Get involved early in cross-functional projects to help shape data requirements and platform needs before implementation begins, rather than inheriting decisions after the fact.
Sit on the Technical Review Board, evaluating major architectural decisions and engineering practices for consistency with platform standards before they are implemented.
Define and maintain enterprise data standards - naming conventions, documentation requirements, and data quality benchmarks - that apply consistently across the platform.
Own the environment architecture: dev, test, and production hierarchy, including promotion workflows and the guardrails that prevent lower-environment changes from reaching production without review.
Define storage optimization patterns: clustering keys, table design decisions, and partition strategies that keep query performance predictable as data volumes grow.
Govern data masking policies, dynamic data access controls, and network-level security configurations to meet internal security standards and external compliance requirements.
Deep experience designing cloud data warehouse schemas and medallion architectures. You have built and maintained bronze, silver, and gold layers in a production environment.
Strong transformation skills: you know how to structure a transformation project for long-term maintainability, not just correctness on day one.
Hands-on experience with cloud orchestration platforms and the failure modes that matter in production: retry logic, alerting, dependency management, and error handling.
Experience designing and governing a semantic layer, centralizing metric definitions, managing change, and keeping business logic out of individual reports.
Comfortable setting standards and getting consistent adoption without becoming a bottleneck on every decision.
Can explain architecture decisions to non-technical stakeholders without losing the nuance that makes the decision defensible.
Treats compute cost and reliability as real deliverables with the same weight as feature work.
7 years in data engineering or analytics engineering, with at least 3 years in a senior technical or architecture role.
Experience in SFR, multifamily, or real estate data environments. Familiarity with operational real estate data is an advantage.

Nice-to-haves

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Platform: workday
Effort: long

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