Note: The job is a remote job and is open to candidates in USA. TradeStation is an online brokerage firm and trading ecosystem focused on delivering trading experiences for active traders and institutions, with an AI-first culture. The VP, Enterprise Data & Analytics will lead the company’s data science, analytics, data products, and enterprise data foundations, setting strategy, driving data-enabled business outcomes, enabling AI initiatives, and building a multi-tier data organization.
Responsibilities
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
Skills
- Remote Position - must reside in Florida, Texas, Illinois, New York, New Jersey, Colorado, Idaho, Massachusetts, Michigan, Minnesota, Missouri, North Carolina, South Carolina, Utah or Virginia
- * 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)
- * 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
Benefits
- Yearly bonus
- Comprehensive benefits for you and your family starting Day 1
- Unlimited Paid Time Off
- Flexible working environment
- TradeStation Account employee benefits
- Full access to trading education materials
- Collaborative work environment
Company Overview
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