Big Data Development Engineer Intern

About Us

Established in 2018, Bybit is one of the world’s leading cryptocurrency exchanges and digital financial platforms, serving over 80 million users across more than 200 countries and regions. Powered by world-class technology and a user-first mindset, Bybit delivers a seamless ecosystem across trading, payments, wealth management, custody, institutional services, and Web3 — connecting users to the future of digital finance.   Our core values define how we build. We listen, care and improve to create products and experiences that put users first. Backed by a global team of ambitious builders, problem-solvers, and innovators, we foster a high-performance and fast-moving environment where talent is empowered to drive real impact at the global scale. Supported by 24/7 multilingual customer service and a strong commitment to innovation, we are shaping the future of finance through technology, collaboration, and bold execution.   Today, Bybit is recognized as one of the most trusted and transparent platforms in the digital asset industry, continuing to expand its global presence while building the infrastructure for the next generation of financial services. Key Responsibilities:

1. Research & Benchmarking — Survey industry and open-source approaches in AI + data governance (metadata & lineage platforms, semantic layers, data quality frameworks, NL2SQL / NL2Metric); produce a best-practice proposal adapted to our tech stack.

2. Data Model Governance — Build AI-assisted model review capabilities: naming convention and layering (ODS/DWD/DWS/ADS) validation, duplicate and redundant model detection, lineage-based identification of unused / low-value / high-cost assets, and automated refactoring recommendations.

3. Metric Semantic Automation — Automatically extract and standardize metric definitions from SQL, lineage, and documentation; detect definition conflicts and redundant builds of the same metric across different reports; maintain a machine-readable semantic layer to support natural language metric queries.

4. Data Quality Automation — Auto-generate quality rules based on data profiling and lineage (replacing hand-written rules); perform anomaly detection on data volume, distribution, timeliness, and schema drift; conduct root cause analysis along lineage; implement automated alert grading and remediation recommendations (or auto-remediation).

5. MVP Delivery — Run at least one end-to-end implementation across the three areas: problem definition → solution design → prototype → deployment on a real data domain → quantified results (coverage, precision/recall of issue detection, manual effort saved) → iteration.

6. Documentation & Communication — Produce solution designs, evaluation methodologies, and results; present findings to platform and data stakeholders; deliver a reusable framework rather than one-off scripts.