Sr.Manager-Data Platform Engineer

Key Responsibilities

• ​ ​ Investment Data Foundations • Lead the design and implementation of investment data models and operational data stores, built from the ground up to support regulatory, operational, and analytical use cases. • Design foundational operational data stores (e.g., source aligned stores, connected data stores, and domain level aggregates) that enable consistent and scalable data consumption across investment workflows. • Define and own investment data taxonomy, including canonical definitions, naming standards, entity relationships, and domain semantics across holdings, positions, transactions, accounting, and reference data. • Apply deep investment domain knowledge (e.g., IBOR, ABOR, custody, holdings, positions, transactions, security and product reference data) to ensure models accurately represent real world investment behavior and lifecycle events. • Ensure data models are designed for long term extensibility, supporting new products, strategies, custodians, and regulatory requirements without re architecture. • Partner with Data Governance and Architecture teams to align domain models with enterprise data standards, classification, lineage, and quality expectations. • Balance domain specific fidelity with platform wide consistency, avoiding bespoke or siloed designs. • Review and approve data design artifacts to ensure clarity, correctness, and operational usability prior to production release. Data API Design & Delivery (API Focused) • Lead the design and delivery of Data APIs that expose investment domain data as reliable, well defined services for downstream consumers. • Define and enforce API best practices, including contract clarity, schema discipline, versioning, and backward compatibility, enabling safe evolution over time. • Ensure APIs meet expectations for availability, performance, security, and operational supportability. • Drive API documentation, discoverability, and onboarding for internal consumers. • Partner with platform teams to ensure APIs integrate cleanly into the broader SAMDA ecosystem. • Contribute to the development and standardization of shared API patterns and reusable components. Scalable Data Capabilities & Platform Leverage • Propose and design reusable data capabilities that address investment domain needs while scaling across the broader SAMDA platform. • Identify patterns and abstractions from domain implementations that can be generalized and reused across multiple SAM data domains. • Partner with Platform Engineering and Architecture to: o Validate scalability and reusability o Align solutions with long term platform direction o Avoid domain specific point solutions where shared capabilities are appropriate • Contribute domain driven enhancements back into shared data frameworks, models, and APIs. • Escalate design trade offs when domain requirements conflict with platform consistency. Platform Alignment, Security & Governance • Ensure all investment data and Data APIs comply with platform standards, data classification rules, information barriers, and governance expectations. • Support architecture, risk, and governance reviews by providing clear data models, taxonomy definitions, lineage explanations, and API contracts. • Identify gaps in platform data or API capabilities and recommend improvements. Operational Readiness & Production Support • Own technical production readiness for investment domain data assets and APIs, including: o Monitoring and alerting readiness o SLA alignment o Incident triage and root cause analysis • Ensure runbooks, dashboards, and operational documentation are accurate and current. • Partner with Platform Engineering and SRE teams to continuously improve stability, observability, and resilience. Delivery Leadership & Team Enablement • Provide hands on technical leadership to engineers delivering investment domain data and APIs. • Break down complex domain requirements into clear, actionable technical work. • Mentor engineers on data modeling discipline, taxonomy, and service quality. • Maintain high engineering standards through design reviews and code walkthroughs. Continuous Improvement • Identify opportunities to improve data quality, model clarity, performance, and operational robustness. • Reduce technical debt and operational risk across the investment data domain. • Proactively improve alignment between domain delivery and platform evolution.