Data Lead - Central Data Team

YipitData is the leading market research and analytics firm for the disruptive economy and most recently raised $475M from The Carlyle Group at a valuation of over $1B. Every day, our proprietary technology analyzes billions of alternative data points to uncover actionable insights across sectors like software, AI, cloud, e-commerce, ridesharing, and payments.

Our data and research teams transform raw data into strategic intelligence, delivering accurate, timely, and deeply contextualized analysis that our customers—ranging from the world’s top investment funds to Fortune 500 companies—depend on to drive high-stakes decisions. From sourcing and licensing novel datasets to rigorous analysis and expert narrative framing, our teams ensure clients get not just data, but clarity and confidence.

We operate globally with offices in the US, APAC, and India. Our award-winning, people-centric culture—recognized by Inc. as a Best Workplace for three consecutive years—emphasizes transparency, ownership, and continuous mastery.

YipitData isn’t a place for coasting—it’s a launchpad for ambitious, impact-driven professionals.

From day one, you’ll take the lead on meaningful work, accelerate your growth, and gain exposure that shapes careers.

• Ownership That Matters: You’ll lead high-impact projects with real business outcomes
• Rapid Growth: We compress years of learning into months
• Merit Over Titles: Trust and responsibility are earned through execution, not tenure
• Velocity with Purpose: We move fast, support each other, and aim high—always with purpose and intention

If your ambition is matched by your work ethic—and you're hungry for a place where growth, impact, and ownership are the norm—YipitData might be the opportunity you’ve been waiting for.

YipitData's Central Data team sits at the foundation of everything we deliver. We build the standardized data products, methodologies, and systems that power every downstream business — from our investment research and corporate products to our data feeds.

Historically, many teams solved similar data problems independently. Central Data exists to identify those common patterns and build shared solutions that improve quality, consistency, and speed across the company.

As a Central Data Lead, you'll own one of these foundational data domains end-to-end. This is a highly analytical product ownership role that combines deep data expertise, systems thinking, technical leadership, and cross-functional execution. Rather than solving one-off analytical problems, you'll design the reusable systems and methodologies that enable dozens of downstream teams to move faster with greater confidence.

Each domain is jointly led by a three-person leadership team:

• Central Data Lead — owns methodology, data quality, and analytical strategy
• Technical Product Manager — owns prioritization, roadmap, and business alignment
• Data Engineering Manager — owns engineering execution, platform architecture, and technical delivery

Together, you'll define how your domain evolves while partnering closely with data evaluation, engineering, downstream product teams, and external data partners.

• Consumer Receipts - own the systems that process, classify, and validate transaction-level consumer receipt data across millions of purchases.
• B2B Spend - own the systems that transform complex mid-market and enterprise purchase and invoice data from multiple providers into standardized, production-ready datasets.

Your success won't be measured by how many analyses you complete. It will be measured by how effectively you've built systems that make hundreds of future analyses faster, more consistent, and more reliable.

• Own the lifecycle of your data domain — from defining how raw partner data should be processed, validated, tagged, and modeled to ensuring downstream teams can confidently build products on top of it. Develop deep expertise in your domain and the mental models needed to identify issues before they impact customers.
• Build systems that improve data quality — Design validation frameworks, monitoring, and QA systems that proactively detect issues. Reason deeply about representativeness, bias, and systematic risks—not simply whether individual records look correct.
• Design reusable methodologies that scale — Identify common business concepts and analytical patterns across Investor, Corporate, and Data Feeds. Build centralized methodologies that reduce duplication, improve consistency, and create lasting leverage across the organization.
• Set analytical and technical direction — Partner with the Technical Product Manager to prioritize investments based on cross-business impact, and with the Data Engineering Manager to shape processing architecture and platform capabilities. Make thoughtful tradeoffs between speed, rigor, automation, and long-term scalability.
• Expand and evolve your domain — Partner with the Data Evaluation team to onboard new datasets and work directly with technical and business stakeholders at our data providers when needed. Build reusable integration patterns that make future dataset onboarding faster and more reliable.
• Redesign analytical work with AI — Use AI, automation, and emerging tooling to fundamentally improve how data is processed, validated, documented, and maintained. Continuously identify opportunities to eliminate manual work and increase the scale and quality of what the team can accomplish.
• Help build the organization — As the team grows, mentor junior analysts and establish the standards, processes, and culture that define how your domain operates.

• Over your first year, you might:

• Design a generalized methodology for classifying millions of receipt line items across multiple data providers.
• Build automated QA systems that detect systematic shifts in merchant tagging before they impact downstream products.
• Develop reusable frameworks that reduce the time required to onboard new datasets from months to weeks.
• Partner with Engineering to redesign processing architecture that improves scalability while reducing operational overhead.
• Create standardized business logic that replaces multiple inconsistent implementations used across different business units.

• You have 6-8+ years of experience in data analytics, with a background in fields like financial services, management consulting, data science, or high-growth technology — or another environment where you worked with complex data to drive high-stakes decisions
• You have expert fluency in SQL and experience using Python or PySpark, including building reliable, reusable analysis workflows
• You have a proven track record of quickly learning complex data methodologies and building strong mental models of how and why data works
• You have led complex, ambiguous projects with multiple stakeholders — scoping the approach, driving alignment, and delivering outcomes — with a strong bias toward action and ownership
• You calibrate rigor to the stakes — you know how much precision a given decision or problem merits, and you don't over- or under-invest
• You reason about bias and representativeness, not just averages — you ask whether dropped rows, inconsistent formatting, or gaps in coverage are systematically skewed before drawing conclusions
• You're skilled at working with messy, inconsistent datasets and evolving schemas — and you bring the detail-orientation and discipline to make that work reliable
• You can clearly communicate complex concepts — including methodology, risks, and tradeoffs — and influence cross-functional partners to move decisions forward
• You're energized by the prospect of building — owning a domain end-to-end today, and mentoring and leading junior analysts as the team grows around you
• You actively use AI tools and are excited about using AI to drive leverage — not just productivity, but fundamentally better and faster ways of working