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

Trade Station 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 Trade Station, 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?

We’re seeking a Enterprise Data Senior Product Manager to lead execution of Trade Station’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.

• 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

• 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…