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Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 brand, metro location, mid-level generalist role, and broad skills attract many qualified applicants.
Requires finance-data knowledge and lineage expertise, moderately limiting transferability across unrelated industries.
Explicit 2+ years, SQL and finance-data experience, and tooling knowledge create moderate filtering for candidates.
Job Description
Structured overview of role & requirementsAbout This Role
Act as a data steward responsible for managing and improving data quality and integrity across business systems, focusing on low to moderate complexity audits and defects.
Analyze data-quality control failures, manage defect lifecycle from intake to closure, and collaborate with cross-functional teams to remediate issues affecting Finance data.
Prepare defect metrics, trend reports, and identify process improvements including automation opportunities using SQL, data-quality tools, and AI-assisted productivity tools.
Minimum Requirements
Minimum 2 years of data quality, data analysis, or data management experience, demonstrated via work experience, training, military experience, or education.
Proficiency in SQL including querying, joining, aggregating, and reconciling structured data.
Experience analyzing data defects, data-quality controls, reconciliations, or root-cause analysis.
Ability to use AI and productivity tools (e.g., Microsoft Copilot) and validate AI-assisted outputs prior to use.
Ideal Candidate Profile
Experience working with financial products, finance operational data, and understanding of data-quality concepts such as completeness, accuracy, timeliness, and reconciliation.
Operationally skilled in managing defect records, status reporting, and communicating findings to both business and technical stakeholders within a global matrix involving India and US-based teams.
Capable of independently handling low to moderate complexity data quality initiatives, applying judgment in triage and remediation activities, and driving continuous improvement in defect-management processes.