Senior Lead Product Manager - Data Products

Siepe is a fast-growing technology company headquartered in Dallas, TX –focused on helping investment managers turn complexity into clarity. We build software and data solutions that give hedge funds and financial services firms the visibility, speed, and confidence they need to make better decisions–faster.

Our platform delivers a unified source of truth that empowers our clients with real-time, actionable insights. We don’t just serve the industry— we help modernize it!

Siepe is profitable, privately held, and growing fast. We offer more than just competitive pay and great benefits—we offer the chance to do impactful work alongside sharp, driven teammates in a culture that rewards curiosity, initiative, and follow-through. Whether you come from finance, tech, or are charting a new path, you’ll find meaningful problems to solve, real ownership, and the momentum to grow your career with purpose.

Siepe is seeking a Senior Lead Product Manager - Data Products to lead the product vision, strategy, and execution for our data foundation. This includes stewardship of our security master, pricing/market data integrations, data quality pipelines, and data management tools used across our platform.

This is a player-coach role. You will directly own critical data domains while influencing and mentoring a multidisciplinary team of product managers and data analysts. You’ll partner closely with Engineering and Operations to define what “great data” looks like and ensure that Siepe delivers the most trusted, accurate, and timely data in the credit industry.

• Own the end-to-end data product strategy for Siepe — including security master, issuer master, loan identifiers, market data integrations, pricing sources, and reference data normalization.
• Establish the long-term data vision, defining how Siepe delivers trusted, timely, lineage-aware data across the entire platform.
• Mentor a high-performing data product team, including PMs and data analysts.
• Define clear ownership models and KPIs for data domains (accuracy, completeness, lineage, timeliness, SLA adherence).
• Lead the roadmap for Siepe’s security master & instrument reference— covering bank debt, private credit, public fixed income, equity, structured products, and hybrid instruments.
• Own market data and pricing integrations, including evaluated pricing, terms/conditions data, rating agency feeds, and agent bank sources.
• Partner with Engineering to design scalable data pipelines, validation frameworks, and reconciliation tools that support complex credit workflows.
• Oversee client-facing and internal data management tools that streamline ingestion, handling, and corrections.
• Work closely with Operations to understand client data pain points, exceptions, break rates, and data workflow inefficiencies.
• Partner with Engineering to define data models, schemas, and transformation logic consistent across the platform.
• Collaborate with the OMS, Compliance, Analytics, and AI teams to ensure reliable, well-governed data powers all downstream experiences.
• Engage directly with clients and industry partners to validate direction and ensure Siepe sets the standard for credit data excellence.
• Partner with our AI/Agent team to automate data quality checks, anomaly detection, reconciliation, and enrichment workflows.
• Champion the use of metadata, lineage, and observability tools to increase trust and transparency in Siepe’s data.

• 7-10+ years working with financial datasets, focused on bank debt, private credit, public fixed income, and structured products.
• Deep expertise with securities reference data, pricing sources, issuer hierarchies, ratings, amortization schedules, contract details, accruals and credit market data vendors (e.g., ICE, Markit, Bloomberg, Refinitiv).
• Experience building security masters, data quality frameworks, or data ingestion pipelines in a fintech or financial services environment.
• Strong understanding of data modeling, normalization, and lineage concepts.
• ven ability to mentor and inspire multidisciplinary teams (PMs, data analysts, data engineers).
• A passion for data — and a clear sense of what “great data” looks like in terms of…