Analytics Solutions - Vice President

Lead discovery for complex data and end-to-end initiatives, distinguishing the intended business outcome from the requested report, dataset, workflow, or tool.

Investigate product definitions, systems of record, identifiers, business rules, interfaces, manual steps, and downstream dependencies to establish current state and identify processing gaps.

Evaluate options within established strategy and architecture guardrails, determining what should be reused, connected, integrated, automated, deferred, or left unbuilt, and where human judgment is required.

Translate needs into implementation-ready requirements covering data structures, sources, identifiers, interfaces, transformation logic, controls, ownership, validation criteria, and open decisions.

Partner with Business, Product, Technology, Data Owners, and Data Stewards to connect trusted data to controlled execution, validate resulting state, and make approved outcomes available for downstream use.

Formalize entities, identifiers, lifecycle stages, metrics, source relationships, assumptions, and limitations using documentation, schemas, analytical logic, operational evidence, and SME input.

Develop reusable patterns for product discovery, source access, identifier linkage, lifecycle and event mapping, validation, synchronization, and onboarding; capture recurring needs to guide foundation-building.

Support governed modernization and team development, preserving required logic while avoiding obsolete, duplicative, or incorrectly placed logic in target platforms.

7+ years in data analytics, strategy, integration, analytical product delivery, management consulting, or a related discipline in complex environments.

Experience leading ambiguous data or analytical initiatives through requirements clarification, solution design, stakeholder alignment, validation, implementation, and adoption.

Demonstrated judgment in evaluating reuse, integration, automation, tooling, AI-assisted versus deterministic methods, and when a solution should be changed, deferred, or avoided.

Experience across heterogeneous systems with challenges involving grain, identifiers, relationships, definitions, source authority, interfaces, or data quality.

Strong SQL proficiency across relational or cloud platforms, with technical fluency to investigate data, validate logic, and work credibly with engineering teams.

Experience defining business metrics or operational state with explicit grain, logic, source data, assumptions, exclusions, and validation criteria.

Experience producing structured documentation, including dictionaries, mappings, lineage, metadata, business rules, version history, validation status, ownership, and limitations.

Strong communication and delivery skills, including translation across business and technical audiences and management of concurrent initiatives with limited supervision.

Financial services or similarly complex regulated-industry experience.

Experience with modern cloud data platforms, particularly Snowflake and AWS services.

Experience with data modeling, integration architecture, APIs, event data, or semantic layers connecting meaning and state across systems.

Experience modernizing legacy ETL, reporting, BI, spreadsheet, or manual data processes while preserving required functionality.

Experience supporting platform migrations and reconciling current-state data, logic, metrics, controls, and target-state requirements.

Experience operating governance forums or approval processes for definitions, linkages, execution events, quality thresholds, or analytical assets.

Experience creating reusable discovery, onboarding, validation, synchronization, or delivery methods that scale across products or domains.

Proficiency in Python or R and a quantitative, technical, business, or information-science degree, or equivalent practical experience.