Senior Data Analyst (Graph focused), Fixed Term - Toronto

WHY DEPT®? We are pioneers at heart. What this means, is that we are always leaning forward, thinking of what we can create tomorrow that does not exist today. We were born digital and we are a new model of agency, with a deep skillset in tech and marketing. That’s why we hire curious, self-driven, talented people who never stop innovating. Our culture is big enough to cope and small enough to care. Meaning, that with people across 20+ countries, we’re big enough to provide you with the best tools, global opportunities, and benefits that help you thrive. While acting small by investing in you, your growth, your team, and giving you the autonomy to solve our clients' problems, no matter where you are in the world. This is a 3 month fixed term salaried contract, located in Ontario Canada. Preferably in Toronto. The Role: The Senior Data Analyst is a hands-on analytical contributor embedded in the engineering team. This role bridges the gap between the data itself and the engineers, product managers, and data analysts building and evaluating the Knowledge Graph. The primary focus is data integrity: validating datasets, verifying query outputs, tracing the root cause of discrepancies, and applying statistical methods to assess data quality across multiple storage technologies. This is a practitioner role, not a consulting engagement. The deliverable is evidence — validated results, documented defects, root cause analysis, and statistical assessments that the team can act on. What You’ll Do: Validate datasets loaded into each technology to confirm completeness, accuracy, and structural integrity relative to source data Verify benchmark query outputs across technologies — confirm that the same logical query against the same underlying data produces consistent, correct results regardless of which system executes it Identify, document, and trace the root cause of data discrepancies and defects discovered during validation; distinguish between ETL issues, schema translation errors, technology-specific behavior, and upstream data quality problems Develop and maintain validation test cases and expected outputs for benchmark queries and compliance use cases Support the collection and documentation of compliance use cases from the business unit Apply statistical methods to evaluate dataset representativeness, sampling quality, and measurement reliability across benchmark runs Analyze benchmark result distributions — identify outliers, assess variance across cold/warm/concurrent runs, and flag results that require deeper investigation before scoring Produce summary statistics and data quality reports that inform the team's architecture assessment Document validation findings, defect reports, and root cause analyses in a format the engineering team can act on Maintain a running record of known data issues and their resolution status across each technology under evaluation What You Bring: Demonstrated experience validating large, complex datasets — identifying discrepancies, tracing root causes, and documenting findings clearly Strong SQL skills; ability to write analytical queries against relational databases (PostgreSQL experience preferred) Experience working with data at significant scale — hundreds of millions of records — where manual spot-checking is insufficient and systematic validation approaches are required Familiarity with ETL pipelines and the types of data quality issues that arise in data loading and transformation Practical experience applying statistical methods to data quality assessment: distribution analysis, outlier detection, variance analysis, sampling validation Ability to interpret benchmark result data and distinguish meaningful performance differences from noise Comfort working across multiple database technologies and query languages — this role will need to query data in PostgreSQL, graph databases, and Databricks as part of normal validation work Experience with Databricks or similar distributed data platforms…