Data Lead - Central Data Team

YipitData

1d•$170,000 - $185,000•Remote

About The Position

YipitData's Central Data team is responsible for building standardized data products, methodologies, and systems that support all downstream business functions, including investment research, corporate products, and data feeds. The team aims to identify common data problems and develop shared solutions to enhance quality, consistency, and speed across the company. As a Central Data Lead, you will own a foundational data domain end-to-end, combining deep data expertise, systems thinking, technical leadership, and cross-functional execution. Your role will focus on designing reusable systems and methodologies to empower downstream teams, rather than solving individual analytical problems. Each domain is co-led by a three-person team: the Central Data Lead (methodology, data quality, analytical strategy), a Technical Product Manager (prioritization, roadmap, business alignment), and a Data Engineering Manager (engineering execution, platform architecture, technical delivery). You will collaborate with data evaluation, engineering, downstream product teams, and external data partners to evolve your domain. YipitData is hiring two Central Data Leads: one for Consumer Receipts (processing, classifying, and validating transaction-level consumer receipt data) and one for B2B Spend (transforming purchase and invoice data into standardized datasets). Success will be measured by the effectiveness of the systems built to improve the speed, consistency, and reliability of future analyses. This role is remote-friendly within the US.

Requirements

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
Expert fluency in SQL and experience using Python or PySpark, including building reliable, reusable analysis workflows
Proven track record of quickly learning complex data methodologies and building strong mental models of how and why data works
Led complex, ambiguous projects with multiple stakeholders — scoping the approach, driving alignment, and delivering outcomes — with a strong bias toward action and ownership
Calibrate rigor to the stakes — you know how much precision a given decision or problem merits, and you don't over- or under-invest
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
Skilled at working with messy, inconsistent datasets and evolving schemas — and you bring the detail-orientation and discipline to make that work reliable
Can clearly communicate complex concepts — including methodology, risks, and tradeoffs — and influence cross-functional partners to move decisions forward
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
Actively use AI tools and are excited about using AI to drive leverage — not just productivity, but fundamentally better and faster ways of working

Responsibilities

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.