Senior Manager-Data Platform Engineer TechLead

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: 
oValidate scalability and reusability
oAlign solutions with long term platform direction
oAvoid 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: 
oMonitoring and alerting readiness
oSLA alignment
oIncident 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.