QA Engineer
About DeepLight AI DeepLight AI is a specialist AI and data consultancy dedicated to transforming the regional corporate landscape through bespoke, high-impact intelligent systems. Based in the UAE, we partner with organizations across diverse sectors – with a deep-rooted expertise in Financial Services and Banking – to bridge the gap between complex data and actionable business strategy. At DeepLight, we don't believe in \"off-the-shelf\" fixes. We deliver tailored AI solutions designed to integrate seamlessly into existing enterprise architectures, ensuring that innovation is both scalable and secure. From building robust data foundations to deploying sophisticated AI platforms, we empower our clients to lead in an increasingly automated world. Role Overview As a QA Engineer, you own how we prove our platforms work end to end – from the pipelines that move and transform data to the API layers that expose it. This is a verification role: you don't build the pipelines, you build the confidence that they are correct. You won't just be \"checking boxes\"; you will be thinking critically about how data flows from source to insight and out to the consumer, ensuring every transformation and every endpoint maintains the integrity required for high-stakes financial services environments. Responsibilities - Designing, building, and owning automated test suites – functional, integration, and end-to-end – embedded into CI so quality gates run continuously. - Testing the platform's API layer through contract testing, schema validation, and end-to-end checks against externally-consumed and partner-facing data APIs. - Performing rigorous source-to-target reconciliation and checksums to ensure no \"silent failures\" occur during complex AWS Glue or Data Factory transformations. - Extending and running Soda Core checks across Bronze, Silver, and Gold layers, and verifying that quality gates fire correctly as part of your automated suites. - Monitoring automated alerts, performing initial root‑cause analysis on data drift or schema failures, and collaborating with squads to implement permanent countermeasures. - Working alongside Data Engineering squads to embed automated validation into live data flows, and supporting the data contracts and OpenMetadata lineage that producers and consumers rely on. Benefits - Competitive salary - Comprehensive personal health insurance - Visa sponsorship for the successful individual - Professional development and certification support - Subscription reimbursement relating to your role - Opportunity to work on cutting‑edge AI projects - Monthly employee incentive program - Career advancement opportunities in a rapidly growing AI company Requirements - 2–4 years of experience in QA / test automation, ideally on data‑intensive or backend platforms. - Strong proficiency in SQL and Python for test automation, data validation, and scripting. - Practical experience with API and contract testing (e.g., REST, schema/contract validation). - Hands‑on experience designing and maintaining automated test frameworks and CI‑embedded suites. - Practical understanding of Lakehouse architectures and the Medallion (Bronze/Silver/Gold) design pattern. - Hands‑on experience with AWS services (specifically S3, Glue, and Athena) or similar cloud data stacks. - A degree in Computer Science, Data Science, Information Systems, or a related field. - The ability to look beyond a failed check to understand the root cause and its broader business impact. - An uncompromising eye for accuracy, ensuring referential integrity, reconciliation, and checksums are perfectly aligned. - Comfortable working in fast‑paced environments using Jira and participating actively in Scrum ceremonies. - The ability to communicate technical issues clearly and constructively, fostering a culture of quality within the engineering team. Nice‑to‑Have - Experience testing externally-consumed or partner-facing APIs, particularly in regulated data-sharing contexts. - Hands‑on exposure to data-quality tooling such as Soda Core or equivalents. - Prior exposure to OpenMetadata or other lineage and metadata management tools. - A proactive drive to identify repetitive tasks and develop scripts to increase team efficiency. - Familiarity with the specific data challenges found within the Financial Services or Banking sectors. Commitment to Diversity and Inclusion At DeepLight AI, we recognise that diversity drives innovation. We are committed to fostering an inclusive environment where individuals with different thinking styles can thrive and contribute their unique strengths to our specialised AI and data solutions. Our goal is to ensure