Data Workflow Analysis & Optimization, VP II - State Street Investment Management

Role Overview

The VP II, Data Workflow Analysis & Optimization will lead the design, documentation, analysis, and optimization of data governance and data operations workflows across business, data product, and technology domains. This role will help improve how the organization documents data lineage, manages metadata, identifies and resolves data fragmentation, defines data quality capabilities, and embeds scalable controls into modern data platforms.

The role will play a critical part in advancing a more unified, automated, and platform-enabled data operating model, with a focus on improving transparency, control effectiveness, audit readiness, and business trust in critical data assets. The individual will partner closely with Data Governance, Data Operations, Data Platform Engineering, Data Product, Architecture, Risk, Compliance, Privacy, and business stakeholders to simplify processes and leverage artificial intelligence and emerging technologies to change the way work is performed.

Key Responsibilities

Data Workflow Analysis & Optimization

Analyze current-state data workflows across governance, operations, platform, and business domains to identify inefficiencies, manual handoffs, fragmentation, duplication, and control gaps.

Design and document future-state workflows that improve consistency, scalability, accountability, and operational efficiency.

Establish standard methods for workflow documentation, process mapping, requirements capture, issue escalation, and operational measurement.

Partner with global stakeholders to simplify and standardize data-related processes across domains, platforms, and regions.

Data Lineage & Metadata Management

Design and manage processes for documenting business and technical data lineage across critical data assets, applications, platforms, and data products.

Define standards for lineage capture, review, maintenance, impact analysis, and evidence production.

Support metadata management practices including business glossary, ownership, stewardship, data classification, and asset inventory processes.

Partner with data stewards, architects, engineers, and platform teams to improve traceability from data ingestion through consumption.

Data Fragmentation & Rationalization

Identify fragmentation, inconsistency, duplication, and unnecessary complexity across data stores, reports, applications, controls, and business processes.

Develop recommendations to rationalize workflows, reduce redundant data movement, improve reusable data product adoption, and align with target-state platform strategy.

Collaborate with Architecture, Engineering, and Product teams to support consolidation, standardization, and modernization initiatives.

Data Quality Capability Development

Create business and functional specifications for data quality capabilities, including rule design, monitoring, exception management, remediation workflow, scorecards, and control reporting.

Partner with domain stakeholders to define data quality expectations, thresholds, business impact, severity, and accountability models.

Support implementation of repeatable data quality processes that connect issues, root causes, remediation activities, and executive-level reporting.

Drive continuous improvement in the quality, completeness, timeliness, and usability of critical data assets.

Governance, Privacy, Access & Compliance Controls

Ensure workflow designs align with data governance policies, internal control expectations, privacy requirements, information security practices, and audit disciplines.

Embed data privacy controls, access controls, data classification, evidencing, and compliance considerations into data workflows and platform capabilities.

Partner with Risk, Compliance, Audit, Legal, Privacy, and Information Security teams to support control design, control evidence, and issue resolution.

Promote clear ownership, stewardship, accountability, and operating discipline across data lifecycle processes.

Platform, Tooling & AI Enablement

Support the design and adoption of governance capabilities across modern data platforms and data management tools, including Databricks Unity Catalog, Collibra, Informatica, and related technologies.

Define requirements for catalog, lineage, data quality, workflow, metadata, access control, and governance automation capabilities.

Identify opportunities to use artificial intelligence, Generative AI, workflow automation, and emerging technologies to reduce manual work, improve decision support, and accelerate governance execution.

Partner with Data Platform Engineering, Analytics & AI, and Operations teams to embed governance and control capabilities directly into platform workflows.

Stakeholder Engagement & Change Management

Facilitate workshops, requirements sessions, process reviews, and governance forums with business and technology stakeholders.

Translate complex data governance, data quality, and platform requirements into clear business specifications and actionable implementation plans.

Communicate recommendations, risks, progress, and decision points to senior stakeholders.

Drive adoption of new workflows, tools, standards, and operating model changes across distributed teams.

Additional Skills

Strong Experience In:

Enterprise data platforms and data governance ecosystems, including Databricks Unity Catalog, Snowflake, Collibra, Informatica, Alteryx, and related data tools.

Data lineage, metadata management, data cataloging, business glossary, data classification, and asset inventory practices.

Data quality rule definition, profiling, monitoring, exception management, remediation workflows, and scorecarding.

Workflow design, business analysis, process optimization, requirements documentation, and operating model transformation.

Artificial intelligence, Generative AI, automation, and emerging technologies that improve productivity and change how data work is performed.

Data privacy, access controls, audit evidence, compliance controls, and governance operating disciplines.

Qualifications

Core Experience

10+ years of experience in Data Governance, Data Management, Data Quality, Business Analysis, Process Optimization, Data Operations, or related disciplines.

Strong background in asset management, investment management, banking, capital markets, or broader financial services.

Demonstrated experience designing and managing data lineage, metadata, data quality, and governance workflows.

Experience defining business requirements, functional specifications, operating procedures, controls, and executive-ready documentation.

Proven ability to identify process inefficiencies, resolve data fragmentation, and drive measurable operational improvement.

Technical & Functional Expertise

Strong knowledge of data governance frameworks, data lifecycle management, data stewardship, data ownership, and data product operating models.

Hands-on or implementation experience with governance and data tools such as Databricks Unity Catalog, Collibra, Informatica, and related platforms.

Understanding of data architecture, data models, taxonomies, reference data, master data, reporting environments, and platform modernization concepts.

Ability to define specifications for metadata, lineage, catalog, quality, controls, workflow, and automation capabilities.

Experience with analytics, AI enablement, and process automation preferred.

Regulatory & Risk Awareness

Familiarity with regulatory expectations, financial services control frameworks, data privacy, audit, compliance, and risk management disciplines.

Experience working with audit, compliance, privacy, information security, and risk stakeholders.

Ability to align governance practices, access controls, privacy controls, and operational workflows with internal policies and control requirements.

Leadership & Soft Skills

Strong executive