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

Job Summary

Required Qualifications

• •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
• •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 (Spark, Delta Lake)
• •Strong written communication — validation findings, defect reports, and root cause analyses must be clear enough for both engineers and product stakeholders
• •Ability to work independently under minimal supervision, taking direction from peers rather than requiring structured management oversight
• •Experience embedded in a cross-functional engineering team
• •Validated datasets loaded into each technology to confirm completeness, accuracy, and structural integrity relative to source data in Databricks and CDS
• •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
• •Help assess each use case: can it be fulfilled by a conventional database approach, or does it require the traversal and pattern-matching capabilities of a purpose-built graph database?
• •Contribute analytical rigor to use case triage — this is a cost-and-complexity decision as much as a technical one
• •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
• •Must be able to lift 50 lbs

Desired Qualifications

• •PostgreSQL experience preferred
• •Experience embedded in a cross-functional engineering team