AVP, Complaints Data Strategy, Architecture & Governance

This is a fully remote position, open to applicants in India.

📋 Description

• Define and implement the Enterprise Complaints Data Strategy: Create the target-state vision, operating model, standards, and procedures; drive a multi-quarter roadmap across complaint platforms and intake channels.

• Design the complaints data ecosystem: Develop and execute scalable data architectures, data models, and domain-aligned data products that facilitate analytics, governance reporting, and issue management.

• Evaluate and onboard new data sources: Create and implement repeatable strategies to assess new data sources (quality, completeness, lineage, controls), and recommend/execute optimal remediation strategies to address data quality deficiencies.

• Ensure compliance with Enterprise Data Policy & Standards: Manage Collibra assets (business definitions, lineage, retention, access controls), oversee data dictionaries, and lead the intake/resolution of data issues and incidents as the steward for the complaints data domain.

• Establish and maintain data quality controls and monitoring: Define data quality metrics, conduct periodic data quality audits, and develop/maintain Tableau control and data operations dashboards to monitor health, identify emerging issues, and track KPIs across the complaints data environment.

• Facilitate cross-functional delivery and adoption: Collaborate with Enterprise Complaints and engage with Enterprise Data Lake, Technology, Data Office, Operations, and Governance leaders to prioritize initiatives, align dependencies, and ensure solutions are embraced and sustained.

• Foster advanced analytics and innovation: Provide curated/aggregated datasets that support advanced analytics use cases, including trend detection, anomaly detection, and thematic risk identification.

• Lead projects and mentor junior staff members, and undertake special projects as assigned to support the maturity of the enterprise complaint program, platform modernization, and regulatory commitments.

⛳️ Requirements

• A minimum of 4 years' experience in data engineering, data architecture, and data governance/controls, preferably within a regulated setting (financial services is preferred) OR, in the absence of a bachelor's degree, at least 6 years of hands-on data engineering and governance experience.

• Proven ownership of data strategy and architecture for a critical business domain (from source to curated layers to consumption).

• Over 4 years of hands-on Data Engineering experience constructing scalable pipelines and curated datasets using PySpark/ScalaSpark/SAS.

• Demonstrated capability in designing and conducting complex analyses on large structured and unstructured datasets to identify and resolve data quality or integrity issues.

• Experience in automating and consolidating data from various reporting channels while ensuring robust data quality controls and checks.

• Strong project management, communication, multitasking, independence, and relationship management skills are essential for success.

🏝️ Benefits

• Best-in-class employee benefits and programs that promote work-life integration and overall well-being.

• Opportunities for career advancement and upskilling for all employees to assume leadership roles.