VP/Director – Analytics Production Readiness & Assurance (APRA) 

Job Description 

Director – Analytics Production Readiness & Assurance (APRA) 

Analytics Production Delivery | Aladdin Data 

Role Brief 

Analytics Production Delivery within Aladdin Data is responsible for delivering analytics through production processes that are standard-based, explainable, and resilient to change. Analytics Production Readiness & Assurance (APRA) ensures that analytics, configurations, and production transitions meet defined production standards, and that deviations and tradeoffs are surfaced explicitly and handled through consistent decision processes. 

As a Director, you will define, establish, and lead the APRA function as a durable operating capability. You will partner with Analytics Stewards, Product teams, upstream modelers, and production delivery leaders to drive consistent adoption of production standards, readiness criteria, and posture transparency. You will bring strong technical depth in configuration and metadata data modeling, market and risk analytics domain understanding, and AI/automation fluency to scale oversight through deterministic classification, metrics, and intelligent analysis. 

This role requires the ability to operate with general direction and minimal oversight, lead major initiatives, and drive sustained impact through influence and collaboration across teams. 

Key Responsibilities 

Establish and lead the APRA function within Analytics Production Delivery 

• Define the APRA operating model and roadmap: what “production readiness” means, how it is measured, and how assurance is applied consistently across analytics domains.  

• Lead major cross-functional initiatives to embed readiness and assurance into analytics production delivery workflows, ensuring adoption without unnecessary process friction.  

Partner to drive production standards and accountability across upstream and downstream teams 

• Partner with Analytics Stewards and Product to translate domain standards into implementable readiness criteria and measurable posture signals, ensuring standards are practical and observable in production.  

• Hold upstream modelers and delivery stakeholders accountable to deliver models and implementations to agreed production standards by driving clarity, surfacing gaps, and ensuring ownership is explicit.  

Own the technical framework for standards representation and posture visibility 

• Define and evolve the configuration/metadata data model required to represent: production standards, permissible variables, certification posture, configuration alignment, and transition state in machine-readable terms.  

• Establish deterministic methods for comparing deployed configurations against approved standards and for surfacing deviations objectively and repeatably.  

Build scalable oversight, metrics, and automation (AI/tech fluent) 

• Design and operationalize the core APRA metrics and reporting (posture, deviations, performance signals, decision states), producing executive-ready insights that inform prioritization and risk decisions.  

• Lead adoption of automation and AI-enabled analysis to scale oversight (classification support, anomaly surfacing, trend detection, change summarization), reducing manual effort and improving consistency of decision inputs. 

Ensure explicit decision-making and institutional memory 

• Ensure readiness gaps, deviations from standard, and performance tradeoffs are framed with evidence and brought into the appropriate forums for explicit decisions and clear accountability.  

• Establish decision capture practices so acceptance/remediation outcomes, rationale, and revisit criteria are traceable and reusable over time.  

Lead and develop a small technical team 

• Manage and develop junior technical associates/analysts; set direction, coach technical rigor, and establish reusable analysis/tooling patterns to scale APRA outcomes.  

• Create a high-leverage operating rhythm where oversight scales through systems and automation rather than reliance on heroics.  

Qualifications / Requirements 

Required 

• 10+ years of experience across analytics production, analytics platforms, data engineering, or related technical domains, with a track record of leading major initiatives and delivering sustained outcomes in complex environments.  

• Demonstrated ability to operate with general direction and minimal oversight, providing guidance and recommendations based on deep specialist knowledge.  

• Strong experience partnering across Product, Analytics, Engineering, and Operations, using influence to drive alignment, accountability, and consistent adoption of standards.  

• Strong familiarity with market and risk analytics across financial markets (e.g., pricing, risk, portfolio analytics, model-driven outputs) and the production realities of delivering those analytics to clients. 

• Deep technical fluency in data modeling, configuration frameworks, and metadata, with hands-on capability using SQL and Python to validate data, build metrics, and produce decision-ready insights.  

• Demonstrated ability to translate abstract standards into concrete technical specifications (schemas, validation rules, posture logic, metrics definitions) that can be implemented and operated at scale.  

• Track record of developing people and creating scalable operating practices that increase reach and impact across teams.  

Preferred / Plus 

• Experience managing or designing technical frameworks supporting internal or industry analytics engines (e.g., model integration standards, parameter frameworks, configuration registries, certification/readiness pipelines). 

• Experience applying AI-enabled analytics or automation techniques to operational oversight, anomaly detection, or decision support at scale.