About this role.
YipitData is seeking a senior Data Lead to own a foundational Central Data domain, either Consumer Receipts or B2B Spend. The role combines analytical methodology ownership, data-quality strategy, reusable system design, and cross-functional leadership with product and data engineering partners. The successful candidate will use SQL, Python or PySpark, automation, and AI tooling to improve the reliability and scalability of production data products. This US-remote role also includes mentoring junior analysts and setting standards as the domain and team grow.
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Sample interview questions
I would first define critical data dimensions such as completeness, validity, consistency, timeliness, uniqueness, and representativeness. I would implement automated checks at ingestion and transformation stages, establish baselines by provider and merchant segment, and alert on statistically meaningful shifts. I would also document severity levels, ownership, and remediation paths so that issues are resolved before they affect downstream products.
I would identify the stable business concepts, inputs, assumptions, and decision rules that recur across use cases. I would convert them into versioned definitions, modular transformation logic, testable outputs, and clear documentation, while retaining configurable parameters for legitimate business differences. Before broad rollout, I would validate the system with representative downstream teams and measure whether it reduces duplicate work and improves consistency.
I segment missingness and anomalies by provider, time period, geography, merchant type, customer cohort, and other relevant dimensions. I compare these patterns with expected coverage and evaluate whether excluded records systematically differ from retained records. If the issue changes the composition of the data or materially affects conclusions, I quantify the impact, communicate the limitation, and prioritize mitigation or adjustment.
I begin by clarifying the decision stakes, customer impact, reversibility, and cost of being wrong. For high-stakes outputs, I favor stronger validation, explicit uncertainty, and durable architecture; for exploratory work, I use faster, clearly bounded methods. I make the tradeoff visible to stakeholders, document assumptions, and design a path to harden the solution if an initial iteration proves valuable.
I would target repeatable, high-volume work such as schema documentation, anomaly triage, data-quality investigation, classification support, and test generation. AI-assisted outputs would be incorporated with structured evaluation, human review for consequential decisions, and monitoring for drift or error patterns. The goal would be not only faster execution, but more consistent documentation, earlier issue detection, and greater analyst leverage.
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 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.
Example Projects:
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 Are Likely To Succeed If:
- 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
What We Offer:
Our compensation package includes comprehensive benefits, perks, and a competitive salary:
- We care about your personal life, and we mean it. We offer flexible work hours, flexible vacation, a generous 401K match, parental leave, team events, wellness budget, learning reimbursement, and more!
- Your growth at YipitData is determined by the impact that you are making, not by tenure, unnecessary facetime, or office politics. Everyone at YipitData is empowered to learn, self-improve, and master their skills in an environment focused on ownership, respect, and trust. See more on our high-impact, high-opportunity work environment above!
- The annual base compensation for this position is anticipated to be $165K-$205K. The final offer may be determined by a number of factors, including, but not limited to, the applicant’s experience, knowledge, skills, abilities, as well as internal team benchmarks.
The total compensation package includes a mix of base, bonus, equity, and benefits
This role may be performed fully remotely within the United States. Please note that our US headquarters are located in NYC. If the remote work is performed outside of these offices, income may be subject to New York State tax withholding.
Please note that for this position, we are not able to consider candidates who currently or in the future will require visa sponsorship.
We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender, gender identity or expression, or veteran status. We are proud to be an equal opportunity employer.
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