Senior QA Automation Engineer

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5 days ago

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Atreides

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SaaS • Government • Enterprise

Atreides is a SaaS analytics company that transforms petabytes of geospatial and geotemporal data into clear, actionable intelligence. Their platform ingests and fuses land, sea, air, cyber, and space datasets and applies proprietary attribution, modeling, and behavioral analytics to surface relationships, patterns, and anomalies in near real-time. Atreides serves defense, public sector, and commercial customers, enabling faster strategic and tactical decision-making by delivering curated, explainable insights rather than raw bulk data.

11 - 50 employees

☁️ SaaS

🏛️ Government

🏢 Enterprise

📋 Description

• Develop automated test harnesses for validating Spark pipelines, Iceberg table transformations, and Python-based data flows • Implement validation suites for data schema enforcement, contract testing, and null/duplication/anomaly checks • Design test cases for validating geospatial data processing pipelines (e.g., geometry validation, bounding box edge cases) • Integrate data pipeline validation with CI/CD tooling • Monitor and alert on data quality regressions using metric-driven validation (e.g., row count deltas, join key sparsity, referential integrity) • Write and maintain mock data generators and property-based test cases for data edge cases and corner conditions • Contribute to team standards for testing strategy, coverage thresholds, and release readiness gates • Collaborate with data engineers on pipeline observability and reproducibility strategies • Participate in root cause analysis and post-mortems for failed data releases or quality incidents • Document infrastructure design, data engineering processes, and maintain comprehensive documentation.

🎯 Requirements

• 5+ years of experience in data engineering or data QA roles with automation focus • Strong proficiency in Python and PySpark, including writing testable, modular data code • Experience with Apache Iceberg, Delta Lake, or Hudi, including schema evolution and partitioning • Familiarity with data validation libraries (e.g., Great Expectations, Deequ, Soda SQL) or homegrown equivalents • Understanding of geospatial formats (e.g., GeoParquet, GeoJSON, Shapefiles) and related edge cases • Experience with test automation frameworks such as pytest, hypothesis, unittest, and integration with CI pipelines • Familiarity with cloud-native data infrastructure, especially AWS (Glue, S3, Athena, EMR) • Knowledge of data lineage, data contracts, and observability tools is a plus • Strong communication skills and the ability to work cross-functionally with engineers and analysts.

🏖️ Benefits

• Comprehensive health, dental, and vision insurance plans • Flexible hybrid work environment • Additional benefits like flexible hours • Work travel opportunities • Competitive vacation time and parental leave