Software Development Engineer in Test

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Software Development Engineer in Test (SDET) based in Italy.

As an SDET, you will play a critical role in ensuring the quality, accuracy and reliability of a sophisticated customer data ecosystem. You will design, develop and maintain automated testing frameworks that validate complex data pipelines, integrations and digital customer experiences. Working across modern technologies such as Databricks, mParticle, Braze, OneTrust and ThoughtSpot, you will help protect data integrity and ensure seamless information flows across platforms. You will collaborate closely with Data Engineers and other technical teams to identify defects, investigate anomalies and prevent recurring issues. This is a hands‑on engineering position focused on building scalable automation solutions rather than relying on manual testing. You will join a fast-growing, international and remote‑first environment that encourages ownership, innovation and continuous professional development.

Accountabilities:

• Design and implement automated data validation frameworks to test data contracts, schema compliance and identity resolution processes within Databricks before information reaches downstream activation platforms.
• Develop and maintain automated UI and behavioural testing scripts that simulate real-world customer journeys across web and mobile applications.
• Programmatically validate behavioural events, mParticle tags and OneTrust consent tokens to ensure accurate event tracking and application‑level data collection.
• Test and monitor critical data flows across the Databricks, mParticle, Braze and OneTrust ecosystem, ensuring consistency and reliability between platforms.
• Develop automated integration tests and mock API payloads to verify that actions, user data and audience updates are correctly transferred across systems.
• Design and execute end‑to‑end testing for data collection pipelines, including POS, web, mobile applications and CRM systems, ensuring data is accurately cleaned, standardised and transformed.
• Validate data freshness, processing accuracy and latency against near‑real‑time service‑level agreements (SLAs).
• Establish and maintain automated data quality and hygiene checks to detect corrupted records, inconsistencies and schema drift in near real time.
• Audit data flows and consent management processes to ensure privacy preferences are consistently respected across systems and comply with applicable regulations, including GDPR and UK GDPR.
• Investigate and troubleshoot data anomalies, identify root causes and collaborate with Data Engineers to resolve pipeline failures efficiently.
• Take ownership of defects throughout their lifecycle, implementing regression tests and preventative measures to avoid recurring issues.
• Contribute to improving testing strategies, automation architecture and quality assurance practices across the wider data ecosystem.

Requirements:

• Bachelor's degree in Computer Science, Data Engineering or a related technical discipline.
• Advanced SQL skills, including the ability to write complex validation queries and scripts to identify anomalies across millions of records.
• Strong proficiency in Python for developing automated test suites, building testing frameworks and interacting with APIs.
• Proven experience designing and implementing data test automation frameworks from scratch.
• Hands‑on experience testing within Databricks or an equivalent cloud data warehouse or lakehouse environment.
• Strong understanding of Delta Lake or similar data architectures.
• Experience validating complex data transformations across batch and near‑real‑time or streaming pipelines.
• Familiarity with data orchestration and transformation tools such as Apache Airflow and dbt.
• Experience testing data flows through Customer Data Platforms (CDPs), particularly mParticle, including event ingestion, identity resolution and audience synchronisation with downstream engagement platforms.
• Proven ability to investigate data anomalies, troubleshoot pipeline failures and collaborate with Data Engineers to resolve issues and prevent regressions.
• Experience testing REST APIs and validating data payloads exchanged between systems and platforms.
• Strong analytical and problem-solving skills, with the ability to work independently and take ownership of technical challenges.
• Nice to have: Familiarity with Braze data ingestion methods, particularly the Data Track API and Cloud Data Ingestion (CDI).
• Nice to have: Experience testing integrations between Customer Data Platforms and engagement platforms, including mParticle audience synchronisation and