The Technical Debt Engineer will help identify, organize, prioritize, and track technical debt across the organization, focusing on building a reliable data foundation, maintaining an accurate and trusted dashboard, and enabling execution against application rationalization and modernization goals. This role requires a proactive, technically solid individual who owns the data layer — schemas, SQL logic, pipeline integrity, and Tableau reporting — while collaborating with program and leadership teams to keep the portfolio visible, accurate, and actionable. The ideal candidate has strong hands-on experience with SQL, relational databases (Postgres or similar), data pipelines, ETL, Tableau, and Git, and is comfortable working with cloud data platforms such as Snowflake as the program's data infrastructure evolves.

Tech Debt

Strategy, Planning, and Execution Partner with business, product, and technology stakeholders to identify, document, and prioritize technical debt using TIME Classification and 6R frameworks. Facilitate stakeholder discussions to clarify ownership, dependencies, migration destinations, effort sizing, timelines, and execution risks. keep portfolio data accurate and current. Help prepare executive-ready materials communicating tech debt reduction progress, cost savings, and business value.

Own reporting and visualization solutions end-to-end – design, development, maintenance, documentation, and continuous improvement – using Tableau and related tools, ensuring views and reporting layers reflect accurate business logic, application categorization, and stakeholder needs.

Understand the full data ecosystem: analyze data across sources, identify meaningful relationships between systems and entities, and translate that understanding into accurate, trusted reporting logic and dashboard design. Support TCO and savings calculation design and implementation, including licensing, infrastructure, support, and maintenance cost data per application. Maintain clear documentation of data sources, refresh processes, business logic, known limitations,



and user guidance – complete enough that any team member can troubleshoot or extend the solution.

Evaluate internal and external tools, processes, and platforms to support strategic initiatives and inform technical decisions. Support business case development with research, analysis, and data-backed recommendations. Collaborate with cross-functional teams across Marketing, Finance, Revenue Operations, Security, product, engineering, and related functions to gather information, validate assumptions, and support informed decision-making.

Experience in software engineering, data engineering, technical consulting, business technology, product operations, technology operations, or a related role.

Experience with Tableau dashboard development, including views, calculated fields, data source management, and dashboard maintenance. Knowledge of the Salesforce technology ecosystem, architecture, and best practices. Strong understanding of data definitions, data governance, data quality, and structured reporting.

Ability to analyze data across systems, identify meaningful relationships, and translate technical logic into clear, business-friendly language for stakeholders and leadership. Excellent documentation skills, including the ability to explain technical logic, data flows, dashboard functionality, process context, and business impact. Strong stakeholder management skills, including the ability to work with product, technology, business, data, and leadership teams.

Comfort using AI tools to support research, documentation, analysis, and productivity.

Experience with cloud infrastructure (AWS or similar) and integrating complementary data sources to support data pipelines, enrich portfolio data, and validate program outcomes.

Experience evaluating internal tools, vendor solutions, technical platforms, or business case options.



Familiarity with AI agent frameworks or ML-assisted automation, particularly for data quality monitoring, anomaly detection, insight generation, or predictive analytics. Strong SQL skills and hands-on experience with relational databases (Postgres, Amazon RDS, or similar), including data modeling, schema design, reporting logic, ETL, and data pipeline development and maintenance.

Experience with Snowflake or comparable cloud data platforms, including schema creation, table design, and data migration. Proficiency with Git and version control for managing scripts, transformation logic, schema definitions, and documentation in a shared repository.

Technical Ownership

Takes accountability for the data foundation, dashboard logic, reporting accuracy, and technical maintainability of assigned workstreams. Identifies technical gaps, data risks, and process weaknesses before they become blockers. Data Integrity and Analytical Thinking Understands complex data relationships and creates clear, meaningful structures across application, platform, ownership, cost, roadmap, and status data.

Maintains high standards for data quality, accuracy, traceability, and completeness. Uses analysis to generate practical insights and support business decisions. Identifies risks, blockers, data gaps, and next steps independently.

Drives work forward with minimal supervision. Communicates clearly and proactively with technical and non-technical stakeholders. Translates technical concepts into business-friendly language. Provides concise updates, flags risks early, and asks thoughtful questions.

Creates materials that are clear, structured, and appropriate for leadership audiences. Balances quality with urgency and keeps deliverables moving. Build and maintain a trusted data foundation – schemas, SQL logic, validation processes, and refresh cycles – that reporting and program decisions can reliably depend on.

Own data pipeline lifecycle such that data flows reliably from source systems to reporting with documented configurations and no single points of failure.

📌 Technical product Engineer (f/m/d) (Toronto)
🏢 Xebia
📍 Toronto