Enterprise Data Products & Solutions Director

We are looking for an Enterprise Data Products & Solutions Director to drive and lead TradeStation's enterprise data strategy end to end. Reporting directly to the VP of Enterprise Data & AI, this role will own data products and solutions across the organization — directing the multi-year vision, driving the operating model, and being accountable for the business outcomes of the entire data domain. This is an individual-contributor leadership role focused on execution, technical authority, and organization-wide influence.

Responsibilities

• Own the enterprise data product vision, strategy, and multi-year roadmap; set direction and investment priorities aligned with TradeStation’s business strategy.
• Be accountable for area-level outcomes, including OKRs and business results of the data function as a whole (adoption, quality, trust, and impact).
• Lead the organization’s transition to “data as a product,” shifting from bespoke, project-based delivery to standardized, governed, reusable data products with clear ownership.
• Define the operating model and standards, including prioritization frameworks, delivery standards, and scalable ways of working across teams.
• Serve as the executive- and Board-facing authority on data; represent the data function in leadership, planning, and Board-level reviews.
• Lead enterprise data product development, setting requirements and direction for data products, pipelines, and platforms; validate solutions with production-grade SQL and Python.
• Own the semantic layer strategy, defining business-friendly metrics, definitions, and data models to ensure a single source of truth across analytics and reporting.
• Shape data architecture direction with Engineering, modernizing data models, pipelines, and workflows in Databricks or Snowflake.
• Drive the business value of data products, identifying opportunities to turn proprietary and market data into high-value internal or externally facing solutions.
• Own third-party and market data sourcing, evaluating, integrating, and managing relationships and licensing for external data sources.
• Establish and own enterprise data governance, designing frameworks for data classification, quality standards, lineage tracking, access controls, and metadata management.
• Drive enterprise data quality and trust, defining monitoring, remediation strategies, and quality KPIs; hold teams accountable for reliable data.
• Leverage AI for data quality and pipeline optimization, operationalizing AI tools (e.g., Claude, LLMs) to automate profiling, anomaly detection, documentation, and troubleshooting.
• Ensure regulatory and compliance alignment, partnering with Compliance and Risk to meet financial services requirements and internal policies.
• Build enterprise self-service data capabilities, directing documentation, training, and tooling to enable stakeholders to discover, understand, and use data effectively.
• Influence and align across Product, Engineering, Data Science, and business teams with authority and consensus-building.
• Raise organizational capability in data product practices, mentoring PMs, analysts, and engineers to establish reusable systems and clarity.
• Drive adoption and measure impact, tracking product usage, quality improvements, and business outcomes; iterate based on feedback.
• Shape enterprise data strategy and thought leadership, guiding decisions on platform trends, architecture, and capability investments.
• Communicate effectively across all levels, presenting technical strategy to executives and business strategy to engineering teams.
• Maintain deep expertise in modern data platforms, governance tooling, AI-enhanced workflows, and emerging best practices.

Requirements

• Proven ability to own an entire data domain or portfolio, set multi-year strategy, and be accountable for area-level business outcomes.
• Track record driving organizational change (e.g., productizing data) and standing up enterprise data capabilities from vision through execution.
• Executive presence and influence, with experience advising at the executive and Board level and aligning cross-functional teams.
• Expertise in building data product roadmaps, defining requirements, success metrics, and delivering iterative value in agile environments.
• Highly proficient in SQL (complex queries, performance optimization, data modeling) and Python (validation, scripting, automation).
• Deep hands-on experience with Databricks or Snowflake, including modern data architectures (lakehouse, medallion architecture, data mesh concepts).
• Experience designing and implementing semantic layers (e.g., dbt metrics, Cube, AtScale, LookML) and translating business concepts into reusable definitions.
• Proven track record institutionalizing governance frameworks, quality monitoring, metadata management, and compliance controls in complex organizations.
• Hands-on experience leveraging AI tools (Claude, LLMs) to improve data quality, automate documentation, and optimize pipeline development.
• Strong understanding of how data products enable downstream use cases (BI, ML, operations) and familiarity with data monetization models.
• Experience with trading data, market data, regulatory reporting, or financial services compliance (strongly preferred).
• Demonstrated experience owning and leading a data domain end-to-end, delivering organizational change at scale in large, matrixed, or regulated environments.

Qualifications

• Bachelor’s degree in business, finance, computer science, data science, or a related technical field.
• 10+ years in data product, platform, or data strategy roles, including ownership of an enterprise data area with accountability for measurable business outcomes.
• Advanced degree (MBA or technical master’s in CS, data science, or engineering) preferred.

Skills

• Advanced proficiency in SQL and Python for data engineering, scripting, and automation.
• Experience implementing enterprise data governance and quality frameworks in production environments.
• Experience designing semantic layers, metrics frameworks, or business-friendly data models at enterprise scale.
• Experience defining data product value/monetization models or managing third-party and market data sourcing and licensing.
• Experience leveraging AI/LLMs for data quality, documentation automation, or pipeline optimization.
• Familiarity with data governance tools (data catalogs, lineage tracking, quality monitoring platforms).
• Familiarity with data visualization and BI tools (Tableau, Power BI, Looker, Sigma).

Benefits

• Collaborative work environment.
• Competitive salaries and yearly bonus.
• Comprehensive benefits for you and your family starting Day 1.
• Unlimited Paid Time Off.
• Flexible working environment.
• TradeStation Account employee benefits, including full access to trading education materials.

Pay

$197,000–$225,000 (US). Countries outside the US have differing ranges in accordance with local labor markets.