Enterprise Data Sr. Product Manager

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 Who We Are: TradeStation is the home of those born to trade. As an online brokerage firm and trading ecosystem, we are focused on delivering the ultimate trading experience for active traders and institutions. We continuously push the boundaries of what's possible, encourage out-of-the-box thinking, and relentlessly search for like-minded innovators. At TradeStation, we are building an AI-First culture. We expect team members to embrace AI as a core part of their daily workflow, whether that's using AI to accelerate development, enhance decision‑making, improve client outcomes, or streamline internal processes. We hire, grow, and promote people who can harness AI responsibly and creatively. We treat AI as a partner in problem‑solving, not just a tool; following our governance standards to ensure AI is used ethically, securely, and transparently. If you join us, you’re joining a culture where AI is how we work. Are you ready to make yourself at home? What We Are Looking For: We’re seeking a Enterprise Data Senior Product Manager to lead execution of TradeStation’s data strategy and ensure our enterprise data foundation is world‑class. Reporting to the Sr. Director of AI, Data Science & Enterprise Data, you’ll own the roadmap for data products, governance, and platform capabilities that power analytics, AI, and critical business operations across the organization. This role requires a self‑starting product leader who can independently assess our data landscape, identify gaps, and drive solutions from concept to production. The Sr. Product Manager should be equally comfortable writing SQL and Python to validate data quality as you are presenting data strategy to executives. This role will take ownership of hard problems—data quality issues, governance gaps, pipeline modernization, semantic layer design—and drive them to resolution without needing to be told what to do. The Sr. Product Manager will work across Product, Engineering, Analytics, and business stakeholders to build a scalable, governed, and trusted data foundation. The ideal applicant is someone that can see broken data processes and immediately starts fixing them—and excited about leveraging AI to improve data quality and documentation—this role offers the autonomy and impact one is looking for. What You’ll Be Doing: Own the data platform roadmap — independently assess current state, identify gaps, and drive initiatives that establish a solid data foundation for analytics, AI, and business operations Manage the data product development — define requirements and deliver data products, pipelines, and platforms that meet enterprise needs; write production‑grade SQL and Python to validate solutions and set technical direction Help build and maintain the semantic layer — design business‑friendly metrics, definitions, and data models that ensure consistency across analytics and reporting; hands‑on configuration and documentation of semantic layer tools Partner with data engineering to modernize data architecture — design and optimize data models, pipelines, and workflows in Databricks or Snowflake; drive adoption of best practices Data Governance, Quality & Documentation Establish enterprise data governance — design and implement frameworks for data classification, quality standards, lineage tracking, access controls, and metadata management Ensure comprehensive data documentation — create and maintain clear documentation of data assets, business definitions, lineage, and usage patterns; make data discoverable and understandable for all stakeholders Leverage AI for data quality and pipeline optimization — use AI tools (Claude, LLMs) to automate data profiling, anomaly detection, documentation generation, and pipeline troubleshooting Drive data quality initiatives — proactively identify data quality issues, define monitoring and remediation strategies, and hold teams accountable to quality KPIs Ensure regulatory compliance — align data practices with financial services requirements and internal security/privacy policies Self‑Service Enablement & Adoption Build self‑service data capabilities — create documentation, training, and tooling that enable stakeholders across the company to discover, understand, and use data effectively Act as data platform advocate — work with Product teams, Analytics, and business stakeholders to understand needs and translate them into scalable platform solutions Drive adoption and measure impact — track platform usage, quality improvements, and business outcomes; iterate based on feedback Strategic Impact & Communication Shape data strategy — contribute thought leadership on data platform trends, architectural decisions, and capability investments Communicate effectively across levels — present technical concepts to business audiences and business requirements to engineering teams; report progress tied to measurable outcomes Stay ahead of industry trends — maintain expertise in modern data platforms, governance tools, AI‑enhanced data workflows, and emerging best practices The Skills You Bring: Self‑Starter & Strategic Thinker — proven ability to independently assess complex data landscapes, identify high‑impact opportunities, and drive initiatives from strategy through execution without heavy oversight Product Management Excellence — expert at building data product roadmaps, writing clear requirements and acceptance criteria, defining success metrics, and delivering iterative value in agile environments Technical Depth — highly proficient in SQL (complex queries, performance optimization, data modeling) and Python (data validation, scripting, automation); able to review code and set technical standards Data Platform Expertise — deep hands‑on experience with Databricks or Snowflake, including modern data architectures (lakehouse, medallion architecture, data mesh concepts) and platform administration Semantic Layer & Metrics Design — experience designing and implementing semantic layers (e.g., dbt metrics, Cube, AtScale, LookML); ability to translate business concepts into consistent, reusable data definitions Data Governance Leadership — proven track record implementing enterprise data governance frameworks, quality monitoring, metadata management, comprehensive documentation practices, and compliance controls in complex organizations Cross‑Functional Leadership — skilled at influencing without authority; ability to build consensus across Product, Engineering, Analytics, and business teams; comfortable presenting to executives Analytics & Data Product Thinking — strong understanding of how data products enable downstream use cases (BI, ML, operations); familiarity with analytics workflows and tooling Financial Services Domain (strongly preferred) — experience with trading data, market data, regulatory reporting, or financial services compliance requirements Advanced proficiency in SQL and hands‑on experience with modern data platforms (Databricks, Snowflake, or equivalent) preferred Proven track record of implementing data governance and quality frameworks in production environments preferred Experience designing semantic layers, metrics frameworks, or business‑friendly data models preferred Strong Python skills for data engineering, scripting, and automation preferred Experience leveraging AI/LLMs for data quality, documentation automation, or pipeline optimization preferred Experience with data governance tools (data catalogs, lineage tracking, quality monitoring platforms) preferred Familiarity with data visualization and BI tools (Tableau, Power BI, Looker, Sigma) preferred Minimum Qualifications: Bachelor’s degree in business, finance, computer science, data science, or related technical field 7+ years in data product or platform roles, including demonstrated ownership of enterprise data initiatives with measurable business impact Desired Qualifications: Advanced degree in a relevant field (e.g., Computer Science, Financial Engineering, Data Science, MBA) TradeStation provides equal employment opportunities to current and prospective employees, without regard to race, color, religion, sex, national origin, ancestry, sexual orientation, age, pregnancy, disability, handicap, citizenship, veteran or marital status, or any other legally recognized status entitled to protection under federal, state, or local anti‑discrimination laws. #J-18808-Ljbffr