Data Lead - Central Data Team

Coinscapture; Northern (KY); Hybrid; USD 140,000 - 200,000; Full time

Job summary

YipitData is seeking a Central Data Lead to own a foundational data domain end-to-end, combining deep data expertise with systems thinking and technical leadership. You will collaborate with data evaluation, engineering, and downstream product teams to build scalable, reusable data solutions.

This role focuses on designing validation frameworks, improving data quality, and expanding data integrations across Consumer Receipts and B2B Spend domains, while leveraging AI and automation to increase

Qualifications

• Analytical product ownership combining data expertise, systems thinking, and leadership.
• Ability to collaborate across engineering, data evaluation, and product teams.
• Experience driving scalable data architectures and reusable methodologies.

Responsibilities

• Own lifecycle of your data domain from raw data processing to downstream consumption.
• Build reusable data processing, validation, and QA frameworks.
• Set direction with cross-functional partners to prioritize investments.
• Expand domain with new datasets and integration patterns.
• Redesign analytical work with AI, automation, and new tooling to scale.

Skills

Data domain ownership

Systems thinking

Technical leadership

Cross-functional collaboration

Tools

AI tooling

Data pipelines

Job description

About Us

YipitData is the leading market research and analytics firm for the disruptive economy. Our proprietary technology analyzes billions of alternative data points to uncover actionable insights. The world's top investment funds and Fortune 500 companies depend on our data to drive high-stakes decisions.

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.

What It's Like to Work Here:

• Ownership is real, not aspirational. An analyst here can own a data product end-to-end - from methodology to client delivery - and present directly to the investors who depend on it. You won't spend months waiting for "meaningful work."
• Growth is driven by impact, not tenure. Scope and responsibility expand as fast as you can demonstrate you're ready. We promote based on what you've done, not how long you've been here.
• AI is a core working tool, not a side project. We're actively rebuilding how analytical work gets done - using AI agents, automation, and new tooling to fundamentally change what's possible. If you're excited about that, you'll thrive here.

About the Role:

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:

• 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.

We're hiring Data Leads for these two teams:

• 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.

What You'll Do:

• 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 accomp...