Director - Data Architecture

Overview: 
The Architect – Aladdin Data Platforms & Services is a senior technical leader within BlackRock’s Central Architecture organization. This leader is responsible for shaping, governing, and advancing the architecture across the entire portfolio of data platforms and shared services within the Aladdin Data organization. The role blends deep expertise in platform architecture, enforcement of enterprise standards, and delivery accountability. The Architect partners closely with platform engineering, product management, and operations teams to ensure the Aladdin Data ecosystem evolves as a cohesive, interoperable system. The goal is to translate architectural direction into measurable improvements in system reliability, scalability, performance, and developer productivity. Unlike strictly advisory architecture roles, this position carries direct accountability for delivering architectural outcomes and serves as the custodian of enterprise-wide standards, design patterns, and best practices across the Aladdin Data landscape.

Scope of Responsibility: 
This role’s influence spans the entire Aladdin Data portfolio, including (but not limited to) the architecture of:

• Data Platforms and Frameworks: Systems and services for data acquisition, ingestion, transformation, data quality assurance (warranty), distribution, and governance.

• Shared Services and APIs:Platform APIs/SDKs, configuration models, metadata management, observability frameworks, continuous integration/continuous deployment (CI/CD), and other operational tooling that supports the Aladdin Data ecosystem.

• Data Processing Patterns:Batch and streaming data processing architectures, including ETL/ELT pipelines and event-driven/streaming frameworks.

The Architect’s ownership is broad, horizontal, and cross-platform, cutting across multiple systems and teams rather than being confined to a single product or application. This ensures consistent architectural practices and interoperability throughout the Aladdin Data organization.

Core Responsibilities: 

1. Platform & Portfolio Architecture 

• Define and evolve target-state architectures for all components of the Aladdin Data platforms and shared services portfolio, guiding the ecosystem toward a cohesive, scalable, and high-performance future state.

• Establish and socialize architectural principles that ensure interoperability, modularity, standards-based integration, and operational excellence across platforms.

• Own and maintain architectural frameworks and contracts for shared capabilities, including: 

• APIs and SDKs: Ensure consistent interface designs and usage patterns across services.

• Configuration & Metadata Standards: Define how systems describe configurations and metadata for uniformity and integration.

• Data Modeling Conventions: Develop canonical data models and patterns for how data is structured and managed across the platform.

2. Delivery of Architectural Outcomes (Not Just Advice) 

• Translate architectural vision into tangible deliverables by working closely with engineering and product teams. This includes guiding designs and implementations so that architectural intent is realized in actual, adopted platforms and features.

• Drive measurable improvements in key platform metrics. For example, deliver initiatives that: 

• Reduce operational noise and improve the reliability and stability of data platform services.

• Increase throughput and scalability of data processing for high-concurrency and high-volume workflows.

• Accelerate time-to-onboard for new data pipelines, products, and domain datasets, improving developer and user productivity.

• Remain deeply engaged throughout the delivery lifecycle – from initial design and development through to deployment, rollout, and user adoption. This is a hands-on leadership role, not limited to providing guidance at a distance. The Architect will work side-by-side with teams to verify that architectural recommendations are effectively implemented and deliver the expected benefits.

3. Standards & Governance 

• Codify and maintain reference architectures, design patterns, and technical guardrails for all Aladdin Data platforms and services. This includes documenting best practices and engineering standards for code quality, testing, release management, security, and observability.

• Lead architecture review boards and design governance processes, ensuring a bias toward clear decision-making and the enforcement of agreed-upon architecture standards across projects and teams. Decisions are documented and followed up with concrete action items and compliance checks.

• Establish “paved road” development practices – provide well-defined, recommended patterns and frameworks that enable product and engineering teams to move quickly and safely. By offering these proven paths (for example, standard templates, common libraries, or automated infrastructure), the Architect helps teams avoid reinventing solutions and ensures consistency across the enterprise.

4. Engineering Excellence & Enablement 

• Mentor and develop engineering talent and junior architects. Champion modern data platform engineering practices such as DataOps, robust CI/CD pipelines, automated testing, and performance tuning. Guide teams in designing for high availability, fault tolerance, and scalability.

• Drive a culture of continuous improvement and technical excellence.Identify systemic gaps in tools, processes, or architectural approaches that hinder productivity or quality. Lead cross-platform initiatives to remediate these gaps, such as introducing new development tools, refining processes, or improving documentation and training.

• Enable self-service and knowledge sharing. Work closely with teams to ensure they have the frameworks, documentation, and support needed to build and operate data products efficiently. This may involve creating architecture playbooks, conducting training sessions, and facilitating communities of practice.

Technology Expectations (Depth of Expertise): 

The Architect must have deep hands-on expertise and the ability to set standards in several key technology areas:

• Snowflake & Advanced Data Warehousing: Extensive experience with the Snowflake platform, including advanced capabilities such as Dynamic Tables (e.g., understanding refresh semantics and data determinism, and implementing effective monitoring and alerting for data freshness), streaming and change data capture (CDC) patterns for near-real-time data processing, use of zero-copy cloning for managing environments and safe experimentation, and performance tuning and scalability best practices (optimizing warehouse sizing, concurrency handling, and ensuring operational reliability). The Architect should be able to define enterprise-wide Snowflake usage standards and patterns to ensure consistent, safe, and efficient adoption of Snowflake across multiple teams and data domains.

• Data Modeling & Transformation (dbt): Deep expertise in data modeling methodologies and transformation frameworks. A strong preference is given to experience with dbt (data build tool), including proficiency with SQL-centric data modeling, Jinja templating, writing custom macros, developing and using reusable dbt packages, implementing data quality tests, and structuring dbt projects for maintainability and collaboration. The Architect will shape and enforce consistent modeling conventions and reusable transformation patterns across the organization, ensuring that data pipelines are well-documented, tested, and integrated into CI/CD processes for reliable deployments.

• Streaming & Event-Driven Architectures: Proven experience with modern streaming and real-time data processing technologies such as Apache Kafka (event streaming, schema management, designing for fault-tolerance and delivery semantics) and Apache Flink or similar stream processing frameworks (stateful stream processing, management of event time vs. processing time, checkpointing, and operationalizing streaming jobs). The Architect should be capable of designing and integrating event-driven architectures into the broader Aladdin Data platform, ensuring these real-time systems are incorporated with the firm’s standards for data governance, observability (monitoring & logging), data lineage, and operational controls.

• Workflow Orchestration & DataOps: Strong background in workflow orchestration systems (particularly directed acyclic graph (DAG)-driven orchestration tools for batch data pipelines) and DataOps best practices. Experience implementing and managing robust CI/CD pipelines and release management processes (e.g., using tools like Git, Azure DevOps, or similar), as well as establishing comprehensive observability practices (including audit logging, health checks, performance monitoring, and alerting for data workflows). The Architect should also be skilled in developing standardized operational runbooks, playbooks, and escalation procedures to ensure that data platform services operate reliably at scale and that teams can effectively respond to incidents.

Qualifications: 

• Experience:Minimum 12+ years of experience in data platform architecture, data engineering, or building large-scale distributed systems, with a track record of technical leadership in complex, multi-team projects.

• Architecture Leadership:Demonstrated success in defining enterprise or platform architectures and driving the implementation of those architectures across multiple teams or product lines. Ability to balance long-term strategic vision with practical delivery, ensuring that architectural decisions lead to concrete improvements and business value.

• Technical Expertise: In-depth knowledge of modern data technologies and architecture patterns, including relational and cloud-native data platforms, big data processing, streaming architectures, and data integration techniques. Hands-on proficiency with tools and languages relevant to the Aladdin Data tech stack (e.g., Snowflake, Python/Java, SQL, Kafka, Flink, dbt, cloud infrastructure, CI/CD pipelines).

• Leadership & Collaboration: Exceptional communication and interpersonal skills, with the ability to influence and align diverse stakeholders—from engineers and product managers to senior executives—in a matrixed organization. Comfortable driving decisions and building consensus without direct reporting authority, using expertise and persuasion.

• Industry Knowledge: Experience in financial services or other highly regulated industries is strongly preferred, as it provides crucial understanding of data governance, security requirements, and operational rigor needed for success in the Aladdin environment.

S uccess Metrics:(Key Indicators of Success in this Role)
Success in the Architect – Aladdin Data Platforms & Services role will be demonstrated by:

• Unified Standards Adoption:Multiple platform teams embrace and implement shared architecture standards—such as common APIs, consistent data modeling patterns, uniform CI/CD pipelines, and robust operational controls. This results in a measurable reduction in platform fragmentation and duplication of effort across Aladdin Data teams.

• Scalable & Reliable Snowflake Deployment:Snowflake usage grows significantly and safely across both batch and near-real-time workloads. The architect’s guidelines enable teams to leverage advanced Snowflake features (dynamic tables, performance tuning, zero-copy cloning) effectively, leading to improved concurrency handling, enhanced system reliability, and well-governed environment management for data experimentation.

• Enterprise-Wide dbt Standardization:dbt is established as a consistent, governed standard for data transformation across the organization. Teams are using reusable dbt packages, standardized Jinja templates, and shared data modeling best practices, which drive higher developer productivity, better data quality, and simpler onboarding for new data products.

• Integrated Streaming Ecosystem:Real-time streaming capabilities (e.g., Kafka, Flink) are seamlessly integrated into the broader Aladdin Data platform, supported by appropriate operational guardrails, monitoring, and data governance. Teams can confidently implement event-driven architectures with consistent patterns for data lineage, observability, and resilience, expanding Aladdin’s ability to handle both batch and streaming data demands.