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Tier-1 brand, popular Data Engineer title, metro market and broad skillset increase candidate competition.
Core data governance skills transfer broadly, but corporate affairs and pharma governance increase domain specificity.
Explicit 8–12 years requirement plus domain governance and AI tool experience creates strict screening.
Accountable for assessing and improving data readiness and quality for AI use cases within Corporate Affairs to ensure reliable AI outputs.
Partner with data owners, IT, and delivery teams to define data sources, requirements, governance, and support remediation of data quality issues.
Produce reporting on data quality status, readiness risks, remediation progress, and maintain data management practices such as metadata, lineage, and control processes.
Bachelor’s degree in Information Systems, Computer Science, Data Management, Business Analytics, Statistics, Engineering, or related field.
8-12 years of experience in data management, data governance, data quality, business intelligence, analytics support, or related roles.
Extensive experience with AI platforms or toolchains (e.g., Claude, GPT, Azure OpenAI, Langfuse, vector databases).
Proven track record in assessing data readiness, documenting data definitions, improving data quality, and partnering with IT and business stakeholders.
Experienced in corporate environments with strong expertise in data governance, quality controls, and metadata management for AI and advanced analytics.
Capable of operating cross-functionally to coordinate data management and compliance practices supporting AI portfolio delivery.
Experienced in producing clear data quality documentation and reports to enhance stakeholder visibility and drive remediation efforts.