VP, Enterprise Data & Analytics

• Data Science & Advanced Analytics – Own the data science and analytics function, delivering predictive modeling, actionable insights, and advanced analytics that inform product, operational, and business strategy.
• Data Products & Data-Driven Activation – Own the data product portfolio and Data Product Management function; activate the company for data-driven decisions and treat data as a product and a durable competitive advantage.
• Data Foundations for AI – Lay out the vision and build the structure that makes data the fuel for AI: the trusted, governed data foundations, semantic layer, and pipelines that AI agents and agentic brokerage workflows operate from.

What You'll Be Doing:

Set the Vision & Own the Outcomes

• Deliver, not just direct – stay hands-on and personally ship the hardest, highest-leverage data science, analytics, and data-product work, setting the pace and the technical bar by delivering rather than overseeing
• Own the data science, analytics, and data product vision, strategy, and multi-year roadmap – set direction and investment priorities for the entire data domain and connect them to TradeStation's business strategy
• Be accountable for domain-level outcomes – own the OKRs and business results of the data function as a whole: data-driven decisions activated, adoption, quality, trust, insight, and measurable business impact
• Make data TradeStation's competitive advantage – define how proprietary and market data is turned into differentiated products, insights, and decisions the competition can't easily replicate
• Define the operating model and standards – establish the prioritization frameworks, delivery standards, and ways of working that let the data organization scale and run predictably across teams

Data Science & Advanced Analytics (Owned by This Role)

• Build and lead the firm's data science and advanced analytics organization, delivering predictive modeling, trader-behavior insights, and business-impact analysis that directly inform product and business strategy
• Stay hands-on in the analytics – personally shape models, metrics, and analyses, writing and reviewing SQL and Python to prove out solutions and set the technical bar by example
• Establish frameworks for scalable analytics delivery, ensuring insights reliably influence product strategy, operational efficiency, and customer engagement
• Evangelize and activate a data-driven culture across TradeStation, enabling teams with training, best practices, and self-service data and analytics tooling so decisions are made on trusted data

Data Products & Data-Driven Activation

• Own the data product portfolio and the Data Product Management function – set requirements, priorities, and roadmaps, and lead the shift from bespoke, project-based reporting to standardized, governed, reusable data products with clear owners and single sources of truth.
• Drive prioritization into Engineering – funnel enterprise data foundation and pipeline priorities through Data Product Management so the Enterprise Analytics engineering team is building the highest-value work
• Own the semantic layer strategy – define the business-friendly metrics, definitions, and data models that ensure a single source of truth across analytics and reporting
• Drive the value of data products – identify opportunities to turn proprietary and market data into high-value internal solutions and, where applicable, externally facing or monetizable data products; measure and report their business value
• Own third-party and market data sourcing – evaluate, integrate, and manage relationships and licensing for external and market data sources that power TradeStation's data products

Enterprise Data Foundation & Engineering Partnership

• Set the architecture and direction for the enterprise data foundation – cloud data platform (AWS, with Snowflake and/or Databricks), lakehouse and medallion architectures, dimensional and semantic data modeling, and batch + streaming pipelines (Spark, Airflow, dbt) – building on the foundation established at the Senior Director level
• Direct real-time and low-latency data capabilities for trading and market data – streaming ingestion and processing (Kafka / Amazon MSK, Flink) of high-volume market data feeds to power analytics, products, and operations
• Partner with the Enterprise Analytics engineering team, who build and maintain the pipelines and foundation – define what gets built, set standards, and provide hands-on technical direction, while Engineering owns ongoing build and maintenance
• Ensure the foundation is trusted, compliant, and scalable so data flows reliably into analytics, products, and AI workflows across TradeStation

Data Foundations for AI & Agentic Workflows

• Lay out the vision and strategy for how data fuels AI – partner with the AI Product & Solutions team and deliver enablement through data, building the data structure, standards, and foundations that team operates from
• Design the trusted, governed data foundations, semantic layer, and pipelines that AI agents and agentic brokerage workflows depend on – making enterprise data ready to power automated, AI-driven decisions and operations
• Stand up the GenAI data layer – retrieval-augmented generation (RAG) pipelines, embeddings and vector databases, feature stores, and MLOps practices that let models and agents operate on trusted, governed enterprise data
• Partner with the AI Product & Solutions team as the supplier of the data fuel – align on requirements, access, quality, and governance so AI initiatives can move fast on a solid data foundation
• Leverage AI to accelerate the data function itself – operationalize AI/LLM tools to automate data profiling, anomaly detection, documentation, and analytics workflows at scale

Hands-On Technical Leadership

• Operate as a working leader, not just a manager – write and review production-grade SQL and Python, prototype data products and models, and validate solutions personally rather than delegating all execution
• Set technical standards by example – establish patterns, review approaches, and roll up your sleeves on the hardest data problems (governance at scale, quality and trust, platform and semantic-layer design)
• Stay close enough to the data and tooling to make credible architecture and prioritization calls and to partner as a peer with Engineering and Data Science

Data Governance, Quality & Compliance

• Establish and own enterprise data governance – design and institutionalize frameworks for data classification, lineage, access controls, metadata management, and master data management (MDM) as company-wide policy, operationalized through data catalog and governance tooling (e.g., Collibra, Alation)
• Drive enterprise data quality and trust – define monitoring, remediation strategies, and quality KPIs using automated data-quality and observability tooling (e.g., Great Expectations, Monte Carlo); hold teams across the organization accountable to trusted, reliable data
• Own regulatory, privacy, and compliance alignment – ensure enterprise data practices meet SEC/FINRA and financial-services requirements and data-privacy regulations (e.g., CCPA, GDPR), with proper PII handling and internal security policies; partner with Compliance, Risk, and InfoSec and support regulatory examinations and audits

Organizational Leadership & Executive Influence

• Serve as the executive-facing authority on data and analytics – secure buy-in and decisions at the executive leadership level, representing the function in senior leadership forums, executive reviews, and enterprise risk committees
• Build and develop the organization – hire, manage, and mentor Director- and Senior Director-level leaders across Data Science, Data Products, and Enterprise Data; raise the bar for data practices company-wide
• Influence and align across the organization – build consensus and drive decisions across Product, Engineering, Data Science, Enterprise Analytics, and business teams
• Deliver quantifiable business outcomes – efficiency gains, increased trading activity, customer growth, insight-driven decisions, and risk reduction – tracked rigorously against clear KPIs

• Domain Ownership & Executive Leadership – proven ability to own an entire data and analytics domain, set multi-year strategy, manage Director-level leaders, and be accountable for organization-level business outcomes
• Hands-On SQL & Python – personally fluent and current in advanced SQL (complex queries, performance tuning, data modeling) and Python (data manipulation, scripting, automation); able to prototype data products and models, write and review production-grade code, and set technical standards by doing the work
• Modern Data Stack & Cloud – deep hands-on experience with cloud data platforms (AWS primary; Azure or GCP a plus), cloud warehouses/lakehouses (Snowflake, Databricks), transformation (dbt), orchestration (Airflow), distributed processing (Spark), and streaming (Kafka / Amazon MSK, Flink)
• Data Architecture & Modeling – modern architectures (lakehouse, medallion, data mesh), dimensional and semantic data modeling, and design of both batch and real-time/streaming pipelines at enterprise scale
• Semantic Layer & Metrics Design – experience designing and implementing semantic/metrics layers (e.g., dbt Semantic Layer, Cube, AtScale, LookML) and translating business concepts into consistent, reusable, governed definitions and single sources of truth
• Data Science & Machine Learning – experience leading teams delivering statistical modeling, machine learning, and predictive analytics; familiarity with the ML lifecycle and MLOps (model deployment, monitoring, feature stores)
• AI & GenAI for Data – hands-on understanding of LLMs, retrieval-augmented generation (RAG), embeddings and vector databases, and agentic workflows; able to architect the trusted, governed data foundations that AI products and agents operate on, partnering with and enabling AI product teams through data
• Enterprise Data Governance – proven track record institutionalizing governance, data quality and observability, metadata management, lineage, and master data management (MDM) using tooling such as Collibra or Alation, across complex, regulated organizations
• BI & Data Visualization – fluency with enterprise BI/visualization platforms (Tableau, Power BI, Looker, Sigma) and enablement of self-service analytics across the company
• Strategic Data Leadership – track record driving organizational change (e.g., productizing data) and standing up enterprise data and analytics capabilities from vision through execution
• Cross-Functional Delivery with Engineering – proven ability to partner with engineering teams that build and maintain pipelines, driving priorities through a data product management function while providing technical direction
• Executive Presence & Influence – proven ability to influence and advise at the executive leadership level, secure buy-in and decisions from senior executives, and align cross-functional organizations with influence rather than authority
• Outcome-Driven Operator – defines clear KPIs across data and analytics initiatives, tracks progress rigorously, and holds teams accountable for measurable business impact
• Financial Services & Market Data Expertise – deep knowledge of brokerage operations (equities, options, futures) or equivalent broker-dealer technology, market structure, market data feeds and vendors, low-latency/real-time data, and regulatory reporting (SEC/FINRA); familiarity with market data protocols such as FIX a plus
• Demonstrated experience owning and leading a data domain end to end – setting strategy, driving cross-functional and executive alignment, and delivering organizational change at scale.
• Proven record of delivering data platforms, data products, or analytics/ML capabilities from concept to production with quantifiable business impact (e.g., multi-million-dollar value, double-digit efficiency gains, major risk reduction)
• Hands-on proficiency in SQL and Python and demonstrated production experience with a modern cloud data platform (e.g., AWS with Snowflake or Databricks) and the surrounding stack (dbt, Airflow, Spark)
• Experience leading the productization of data or building an enterprise data function in a large, matrixed, or regulated environment preferred
• Proven track record implementing enterprise data governance and quality frameworks in production environments preferred
• Experience defining data product value/monetization models or managing third-party and market data sourcing and licensing preferred
• Experience with real-time/streaming data and market data platforms (e.g., Kafka / Amazon MSK, Flink) in a trading or capital-markets environment preferred
• Experience operationalizing AI/GenAI on enterprise data – RAG, embeddings, vector databases, and MLOps – and leveraging AI/LLMs for data quality, documentation, and pipeline optimization preferred
• Experience implementing data governance and cataloging tools (e.g., Collibra, Alation), master data management, and data-quality/observability platforms (e.g., Great Expectations, Monte Carlo) preferred
• Familiarity with BI/visualization tools (Tableau, Power BI, Looker, Sigma) and a relevant cloud certification (e.g., AWS) a plus

• Bachelor's degree in business, finance, computer science, data science, or a related technical field
• 15+ years of progressive leadership experience in enterprise data, data science, analytics, or data product within fintech, trading technology, or other heavily regulated financial services
• 7+ years in senior leadership roles, including direct management of Director-level leaders and cross-functional teams

• Advanced degree (MBA or technical master's in Computer Science, Data Science, Financial Engineering, or Engineering)