Data Analyst

An established technology company developing an innovative, data-driven AI platform for corporate Technology Intelligence.

The company leverages advanced Generative AI and intelligent Copilot experiences to deliver seamless, context-aware tech analytics to global users.

The company is located in the Center region (highly accessible, situated immediately adjacent to a primary train station) and operates on a hybrid model allowing for two days of remote work per week.

Key Responsibilities-

• Building, managing, and maintaining complex datasets, tracking schema integrity, and driving comprehensive data cataloging initiatives.
• Executing rigorous Data QA protocols, diagnosing data drift, and verifying input accuracy across high-volume data ingestion pipelines.
• Establishing formal data validation processes, data lineage mappings, and governance baselines to serve as the organization's Data Steward.
• Partnering with AI researchers to preprocess data, manage prompt-response grounding datasets, and evaluate the factual accuracy of GenAI outputs.
• Generating data-driven insights and diagnostic reports to optimize internal data pipelines and enhance user-facing analytics.
• Data & AI Ecosystem- Data Analysis, Data QA, Data Stewardship, Data Cataloging, Dataset Management, Data Validation Pipelines, Governance, Generative AI (GenAI), LLM Grounding, and Technology Intelligence.

Requirements-

• Academic degree in Information Systems, Data Analytics, Computer Science, Industrial Engineering, or a highly related quantitative field.
• Proven, hands-on professional experience operating as a Data Analyst, Data QA Specialist, Data Steward, or a highly equivalent data-centric role.
• Strong, practical track record in building, managing, and structuring multi-layered datasets alongside maintaining comprehensive data catalogs.
• Direct technical familiarity or practical exposure to the world of Generative AI, LLMs, or prompt/dataset preparation for AI models.
• Solid engineering intuition regarding data quality metrics, root-cause anomaly analysis, and the creation of structured data pipelines.
• Highly methodical and detail-oriented approach to data integrity, with the excellent collaborative skills needed to work alongside advanced AI engineering teams.