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Emburse • Toronto, Ontario, Canada
Role & seniority: Senior Data Analytics SDET (4+ years in testing/SDET with a focus on data systems)
Stack/tools: Data warehouses: Snowflake, BigQuery, Redshift; Cloud/Azure; Orchestration: Airflow; Languages: Python, SQL; Testing: dbt tests, Great Expectations; Data formats: Parquet, Avro, ORC; Observability: Monte Carlo; BI/analytics tools (Nice-to-have)
Design, implement, and maintain automated tests for data pipelines, ETL/ELT, streaming jobs, and data services
Build and own test automation frameworks, synthetic datasets, data quality checks, and data contracts
Ensure data accuracy, quality, lineage, observability, and collaborate across Data Eng, Analytics Eng, Data Scientists, and product/platform teams; participate in CI/CD and incident post-mortems
Strong SQL and analytical validation skills
Python for test automation and data manipulation (pandas/pyarrow)
Experience testing data pipelines, ETL/ELT, and data services; CI/CD integration; familiarity with cloud data warehouses and orchestration/streaming tools
Understanding of data governance, schema evolution, data lineage, and data quality checks
dbt, data observability platforms (e.g., Monte Carlo), experience with Looker/Tableau or BI tools
ML model testing, A/B test validation, statistical methods
Travel industry experience
Location & work type: Location not specified; Work type: Full-time
At Emburse, you’ll not just imagine the future – you’ll build it. As a leader in travel and expense solutions, we are creating a future where technology drives business value and inspires extraordinary results. Our AI-powered platform helps organizations modernize financial operations, increase visibility, and optimize spend across the enterprise.
Emburse SDETs contribute to the development, quality, reliability and observability of the company’s data platform and analytics products. As a Data Analytics SDET, you will design and implement automated testing strategies that validate data pipelines, ETL/ELT processes, analytical models, metrics, and data services. You will work closely with Data Engineers, Analytics Engineers, Data Scientists, Product and Platform teams to ensure that data is accurate, timely, well-documented and fit for business use. \n
Data testing & validation: Design, implement and maintain unit, integration, regression, pipeline, acceptance and data-quality tests for ETL/ELT jobs, streaming pipelines and data services (batch and real-time).
Automation: Build and own test automation frameworks and test harnesses for data pipelines (e.g., Python/pytest, dbt tests, Great Expectations), including synthetic dataset generators, golden datasets and fixture management.
Data systems: Deep experience testing data platforms and tooling: cloud data systems (Snowflake, BigQuery, Redshift, Microsoft Fabric), relational databases (MS SQL) and cloud providers like Azure , orchestration tools (Airflow), and columnar file formats (Parquet, Avro, ORC).
SQL & code: Expert in SQL for analytical validation and in at least one programming language used for data engineering such as Python. Build scripts, tools and automation to validate schema, data lineage, transformations, aggregation correctness, null/missing value handling and performance characteristics.
Observability & monitoring: Implement automated checks and alert logic for data freshness, schema drift, volume anomalies and metric regressions using observability/data quality tools (e.g., Great Expectations, Monte Carlo, custom monitoring).
Performance & scale testing: Design and run performance, throughput and scalability tests for pipelines and data services; profile and tune ETL jobs and queries to identify bottlenecks.
Data contracts & governance: Work with engineering and product teams to enforce data contracts, contract tests for data APIs, and validate PII handling, access controls and compliance requirements.
Debugging & triage: Investigate, reproduce and document data defects, root causes and remediation plans; apply production debugging skills against ETL jobs, queries and logs.
Process Follow SDLC and agile practices, including writing and reviewing tests as code, peer code reviews, CI/CD pipeline integration of tests, and release gating based on automated data quality checks. Maintain and follow coding and test case management standards; ensure test suites are deterministic, reproducible and fast enough to run in CI and nightly/rolling regression schedules. Document testing approaches, known limitations and runbooks for operational incidents involving data.
Impact Own quality for analytics deliverables and data platform features within your area. Establish test strategies and define appropriate test coverage for pipelines, models and metrics. Drive improvements in data quality and reliability through automation, test architecture, and proactive detection of data problems. Contribute to data documentation (data dictionaries, lineage diagrams, assumptions) and raise the bar for data governance and observability across teams.
Communication Collaborate with Data Engineers, Analytics Engineers, Data Scientists, Product Managers and Platform Engineers to design tests and validate business logic in analytical pipelines. Explain technical tradeoffs, testing coverage and risk to non-technical stakeholders and suggest pragmatic compromises when necessary. Produce clear, evidence-based defect reports and remediation plans; participate in post-mortems focused on data incidents.
Required: Bachelor’s degree in Computer Science or related field, or equivalent years’ experience
Required: 4+ years of testing or SDET experience with a strong emphasis on data systems and analytics. Demonstrated experience writing tests for data pipelines, validating analytical outputs, or building test frameworks for ETL/ELT processes.
Required: Proficiency in SQL and at least one general-purpose programming language used for data processing (Python preferred; Scala/Java acceptable).
Required: Experience with cloud data warehouses (e.g., Snowflake, BigQuery, Redshift), and familiarity with at least one orchestration or streaming technology (Airflow, Kafka, Spark).
Preferred: Experience with dbt, data observability platforms (e.g., Monte Carlo), data modeling, and analytical/BI tools (Looker, Tableau, etc.). Experience testing ML models, A/B test validation, or statistical methods is a plus.
Required: Experience within Travel industry
Strong SQL skills for complex analytical validation: joins, window functions, aggregation correctness and performance tuning. Proficient in Python for test automation, data manipulation (pandas/pyarrow), and building test harnesses. Demonstrable experience designing automated data quality checks and defining acceptance criteria for analytics deliverables. Experience with CI/CD systems (GitHub Actions, Jenkins, CircleCI) and integrating test runs into pipelines. Familiarity with data formats (JSON, Avro, Parquet) and schema evolution strategies. Solid understanding of distributed data processing, consistency, eventual consistency tradeoffs, and data lineage. Ability to reason statistically about datasets—detecting outliers, sampling strategies, and validating model/metric correctness. Excellent debugging skills across code, SQL, job logs and metadata; ability to produce reproducible test cases and remediation paths. Strong collaboration and written communication skills; experience conducting design/code reviews and mentoring peers.
\n Why Emburse?
Finance is changing—and at Emburse, we’re leading the way. Our AI-powered solutions help organizations eliminate inefficiencies, gain real-time visibility, and optimize spend—so they can focus on what’s next, not what’s slowing them down.
A Company with Momentum – We serve 12M+ users across 120 countries, helping businesses modernize
their finance operations.
A Team That Innovates – Work alongside some of the brightest minds in finance, tech, and AI to solve real-
world challenges.
A Culture That Empowers – Competitive pay, flexible work, and an inclusive, collaborative environment that
supports your success.
A Career That Matters – Your work here drives efficiency, innovation, and smarter financial decision-making
for businesses everywhere.
Shape your future & find what’s next at Emburse.
Emburse provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, Emburse complies with applicable state and local laws governing nondiscrimination in employment in every location where the company has facilities. This policy applies to all terms and conditions of employment.