Software Engineer in Test III

Overview

Who We Are: 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.

What you will do
:
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.

• Technical
• 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, Big Query, 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.