Senior SDET — Data Platform / Query Engine

Level: Senior E
ngagement: Long-term contract
Location: Europe (EU / EEA / UK), remote
Working hours: EU business hours, shifted 2–3 hours later to ensure daily overlap with US West Coast (PST/PDT) mornings
About the Client
Our client is a leading enterprise data platform company building an open, high-performance data lakehouse for AI and analytical workloads. The platform combines an intelligent SQL query engine, an AI-ready semantic layer, and an open catalog built on Apache Iceberg — enabling Fortune 500 companies across finance, energy, manufacturing, and logistics to unify, query, and govern data at massive scale across cloud and on-premise sources.
About the Role
We are looking for a Senior SDET to lead the design of automated test frameworks and testing strategy for a large-scale distributed data platform. You will own the quality of the query engine, connectivity layer, and backend services end-to-end — from framework architecture through CI/CD infrastructure across all major clouds — and mentor middle engineers on the team.
This is a data-heavy, backend-focused role. We are not looking for web/UI QA engineers — the work centers on validating distributed query execution, data correctness at scale, connectivity drivers, and backend microservices.
Responsibilities
Architect, design, and evolve automated test frameworks in Python / pytest for backend services, REST APIs, and distributed data components.
Define testing strategy for new features and platform components — coverage, risk assessment, and quality gates.
Own end-to-end, integration, performance, and regression testing covering SQL query execution, data correctness at scale, and platform APIs.
Lead performance and load testing efforts with JMeter, including workloads over JDBC / ODBC / Arrow Flight drivers.
Design and validate large-scale data testing scenarios — query plans, result correctness across heterogeneous data sources, metadata consistency, and behavior under high concurrency.
Own CI/CD test pipelines in Jenkins — architect, maintain, and continuously improve.
Provision and manage test environments in Kubernetes (GKE / EKS / AKS) across GCP, AWS, and Azure using Docker; drive automation and reproducibility.
Investigate complex, cross-layer failures and drive root-cause analysis with engineering.
Mentor middle SDETs, review test designs, and set quality standards across the team.
Partner with US-based development leads on shift-left practices, testability, and release readiness.
Required Qualifications
Education: B.S. or M.S. in Computer Science, Computer Engineering, or a related technical field.
Programming: Strong proficiency in Python, including pytest, with deep understanding of OOP, software design principles, and test framework architecture.
SQL & Data: Advanced SQL skills and strong understanding of relational and analytical data systems, including query execution internals.
Data-intensive testing experience: Demonstrated experience testing data-intensive systems — query engines, ETL/ELT pipelines, streaming platforms, or analytical databases. Candidates with only web/UI QA backgrounds are not a fit.
Testing experience: 5+ years in backend/system test automation, with demonstrated ownership of test strategy and infrastructure.
CI/CD & DevOps: Solid experience architecting and maintaining Jenkins pipelines and test environments.
Containers & Orchestration: Strong working knowledge of Docker and Kubernetes (running workloads, debugging pods, deploying complex environments).
Cloud: Hands-on experience with at least one major cloud (GCP, AWS, or Azure); exposure to more than one is a strong plus.
Version Control: Confident with Git / GitHub workflows and code review practices.
English: Upper-Intermediate or higher (B2+) — daily written and verbal communication with a US-based engineering team.
Availability: Able to work EU hours with a 2–3 hour shift toward US West Coast time to ensure daily overlap with the client team.
Leadership: Prior experience mentoring engineers, defining test strategy, or leading a QA/SDET function.
Desired Skills
Deep experience testing REST APIs and backend microservices at scale.
Hands-on with distributed computing frameworks (e.g., Apache Spark, Kafka) and MPP SQL query engines (e.g., Presto, Trino, or similar).
Strong understanding of modern data lakehouse concepts, open table formats (Apache Iceberg), and data warehousing.
Experience with data connectivity drivers: JDBC, ODBC, Arrow Flight.
Performance testing with JMeter or comparable load-testing tools at production scale.
Kubernetes on managed services (GKE / EKS / AKS) and multi-cloud exposure.
IaC tools such as Terraform.
Deep understanding of query plan generation, query acceleration / materializations, and metadata integrity in distributed data systems.
Prior experience leading a QA/SDET function in a data-platform or database engineering environment.