Job intelligence
Data Lead - Central Data Team
YipitData
Before relying on the listing: Confirm Application closing time with the employer or source. See verification questions
Executive opportunity brief
The decision in one screen
A compact evidence-first read of compensation, flexibility, mobility and what still needs verification. No universal quality score is used.
Fast read
Opportunity DNA
The available details do not point to a single standout advantage yet. The clearest issue to weigh is the salary benchmark is still shallow.
The range is usable, but the comparable sample is still too shallow for a strong relative-pay call.
Geographic eligibility is restricted to a country or region.
One or more material qualification requirements are stated and may narrow the realistic applicant pool.
Sponsorship and relocation support are not clear from the current listing.
Part of the application route can be assessed, but the full external process may contain additional steps.
At least one recent enabled source can still renew this vacancy.
This fingerprint summarizes what is clear, constrained or still unanswered in the current listing. It is a reading aid, not an overall job score.
Decision evidence check
4 of 5 core facts stated
This checks whether the listing states the facts that commonly change an employment decision. It measures disclosure completeness, not whether the opportunity is good or bad.
Facts already stated
- Compensation
- Work arrangement
- Advertised location
- Employment type
What to verify next
- Application closing time: Is there a firm application deadline, a rolling review window or no stated closing date?
Advertised location is not proof of work authorization, sponsorship, or legal eligibility. Unknown information is not a negative score; verify those questions separately.
Decision levers
What could make or break the fit?
Reasons to look closer
Nothing stands out strongly enough yet to call a clear advantage. The role may still be attractive for reasons the listing does not spell out.
Trade-offs to weigh
- The salary benchmark is still shallow: Pay is disclosed, but the current comparison pool is too small for a strong relative-pay conclusion.
- Location rules could change the fit: Geographic eligibility is restricted to a country or region.
- The experience requirement raises the entry bar: The listing appears to require at least about 8 years of experience.
Questions worth asking before you invest more time
- Confirm whether a degree is actually required, preferred, or replaceable with equivalent experience.
- Ask whether visa sponsorship is available before investing significant time in the application.
These questions come from details the listing leaves open. An unanswered point is not treated as a disadvantage.
Salary transparency context
Is pay disclosure typical for this kind of role?
This listing states usable structured compensation, but the current role sample is still too small for a reliable disclosure-rate comparison.
Compensation counts only when amount, ISO currency and supported pay period are all known.
At least ten current comparable roles are required before HPJ publishes a disclosure percentage.
Employer-level comparison is withheld until enough current employer listings are available.
This compares salary-disclosure behavior in current visible listings. It does not compare raw salary amounts across currencies, infer undisclosed pay as zero, or rate employer quality.
Private offer & negotiation lab
Turn an offer into evidence, not guesswork
Enter an annual base offer and, if you want, your own target. HPJ compares only within one explicit currency and never chooses a target for you.
Negotiation evidence available: advertised pay range, strong decision evidence.
Enter an annual base offer to compare it with explicit same-currency evidence. HPJ will not suggest a target for you.
Review the full package · 10 terms
Mark what is confirmed, what needs clarification and what you want to prioritize. These choices and notes stay in this browser.
Usable advertised compensation is available.
Variable compensation is mentioned; verify amount, formula, target and eligibility.
Equity or long-term incentive language is mentioned; verify grant type, vesting and valuation basis.
Retirement or pension benefits are mentioned; verify employer contribution and vesting.
Health-benefit language is mentioned; verify coverage, dependants and employee cost.
Paid-leave language is mentioned; verify the exact entitlement and carry-over rules.
A work-arrangement signal is available; verify schedule details before relying on it.
Relocation support not established
Sponsorship not established
A review cycle is mentioned; verify timing and whether compensation can change at that review.
Questions worth taking into the offer conversation
- What health coverage is included and what costs remain with the employee?
- How much paid leave is provided and what are the carry-over or blackout rules?
- If relocation is relevant, what expenses, timing and repayment conditions are covered?
- What exact sponsorship or work-authorization conditions apply to this role?
- When is the first performance and compensation review after joining?
- Is there a firm application deadline, a rolling review window or no stated closing date?
Skill bridge
What does this role connect to?
The listing contains useful skill signals, while the current occupation sample is still too thin for a broader comparison.
Skills named in this listing
Shared with the current career sample
The occupation sample is still too thin for a reliable shared-skill comparison.
Application edge
- If these skills match your background, prepare one concrete example that shows how you used Python and SQL rather than relying on a keyword-only claim.
- The listing states about 8 years of required experience. Check that the dates and scope in your application make that experience easy to verify.
Skill signals come from explicit text mentions. A mention is not automatically a requirement, and absence from the current top-skill list does not make a skill rare or unimportant.
Application Decision Lab
Turn the listing into a preparation plan
Separate what the listing actually states from what you still need to prepare or verify. Your selections and short notes stay in this browser and are never submitted to High Paid Job.
At least about 8 years of experience required
The available wording contains an explicit minimum/required years-of-experience signal. Preferred years are tracked separately and never promoted into the required minimum.
Python
This skill or technology is explicitly mentioned in the available listing text. A mention is not automatically a mandatory requirement; prepare a concrete example only if it is relevant to your experience.
SQL
This skill or technology is explicitly mentioned in the available listing text. A mention is not automatically a mandatory requirement; prepare a concrete example only if it is relevant to your experience.
Questions worth resolving before you rely on the opportunity
- Application closing time: Is there a firm application deadline, a rolling review window or no stated closing date?
- Work authorization / sponsorship: If sponsorship or work authorization matters to you, what exact immigration or employer-sponsorship conditions apply to this role?
- Application process: What interview, assessment, portfolio, reference or screening stages are part of the hiring process?
Decision analysis
What the listing really tells you
For Data Lead - Central Data Team at YipitData, the most useful clues right now are the range is useful even though the comparison pool is small, the location rules are clear enough to judge, and qualifications reveal more than the job title alone. Looking at them together makes the trade-offs easier to see.
The range is useful even though the comparison pool is small
The USD range can be compared directly within its currency, although the present comparison sample is too shallow for a confident relative-pay ranking.
The location rules are clear enough to judge
The work-location pattern is “Remote with some uncertainty or restrictions”, with Remote Freedom at 62/100. The most important location constraint is geographic eligibility is restricted to a country or region..
Qualifications reveal more than the job title alone
At least about 8 years of experience required · 1 additional stated barrier. Unstated education or experience requirements stay open rather than being assumed.
The visible application effort can be estimated
Provider-mediated application path. The external application may contain additional steps that are not visible from the listing itself.
The listing can be traced to a current source
The opportunity currently has 1 renewable source path across 1 source classes. Multiple independent paths can strengthen continuity, but they are not a substitute for checking the application destination.
How this analysis is assembled
The brief pulls together the parts of the vacancy that can be compared reliably today. Gaps in the listing remain visible instead of being filled with assumptions.
Analysis evidence coverage
How much analysis can the current evidence support?
This title does not yet fit a reliable comparison family, so a pay percentile is not shown.
Measures observable location flexibility and explicit restrictions; it does not determine whether remote work is personally desirable.
The configured source supplied structured salary fields, a usable currency and a focused comparison value.
The source posting date is today.
At least one enabled source recently observed the vacancy. The source chain is traceable, but this is not independent employer verification.
There is no universal opportunity score because pay, flexibility and access matter differently to different people. Compare Jobs lets you weight the evidence around your own priorities.
Decision checklist
What is supported, uncertain or worth checking?
This checklist separates supported strengths, visible cautions and missing evidence. It is not an overall recommendation or guarantee that the job is suitable for you.
Supported strengths
No standout strength is supported strongly enough to list here. That is not a negative rating.
Visible cautions
- Geographic eligibility is restricted to a country or region.
- The listing appears to require substantial prior experience.
- This is remote work with a geographic restriction, not a worldwide-remote vacancy.
- At least about 8 years of experience required
Still unknown
- There is not enough comparable salary evidence to establish this job’s current pay position.
- Visa sponsorship is not established by the available listing data.
Pay vs. barriers
Opportunity Efficiency not assessed
The current pay position is not reliable enough to weigh compensation against the stated barriers.
1 explicit required barrier is visible in the available wording.
The listing states at least about 8 years of required experience.
1 explicit Remote Freedom restriction signal is present and is shown separately from qualification requirements.
- Current comparable pay position is unavailable.
- Education requirement is not established.
This section weighs pay context against clearly stated barriers without collapsing unlike requirements into one universal score. Degree, experience, licensing, clearance and location limits remain separate, and missing qualification details stay unknown.
Beyond the headline
Not enough evidence for a hidden-value finding
Potential hidden value appears only when several distinct non-pay advantages are actually supported. Missing requirements or benefits are never counted as positives.
Still check: At least about 8 years of experience required · The current application path is provider-mediated rather than directly classified as an employer-authorized or official route.
Hidden Value is an experimental evidence summary, not an investment-style score or guarantee of employability. It only uses explicit current listing evidence and does not infer that an unmentioned qualification is unnecessary.
Explainable opportunity highlight
Why this opportunity stands out
These are supported signals from the current listing and comparison sample—not a guarantee that this is the best job for you.
The source provides structured salary information with a comparatively focused range, improving the usefulness of pay comparisons.
The source posting date is today.
Compensation intelligence
How competitive is this pay?
This title does not yet fit a reliable comparison family, so a pay percentile is not shown.
Why this analysis?
- This title does not yet fit a reliable comparison family, so a pay percentile is not shown.
- Salary confidence: High. The configured source supplied structured salary fields, a usable currency and a focused comparison value.
- Range quality: Focused salary range.
Employer compensation communication
Pay transparency at YipitData
Very strong current pay transparency
This score uses the employer’s current visible listings and combines how often pay is disclosed with how useful those ranges are. Bonus, equity and benefit mentions are shown separately and do not raise the score.
This pattern comes from the employer’s current visible vacancies. It describes hiring and pay transparency in those listings, not employee satisfaction or a permanent judgement of the employer.
Current employer intelligence
YipitData — current opportunity snapshot
Employer Evidence Profile
How often this employer’s current visible sampled vacancies state decision-critical information. These percentages describe evidence coverage, not employer quality.
Current sampled listings with advertised compensation evidence.
Current sampled listings whose work arrangement is clearer than unknown.
Current sampled listings with a supported country signal.
Current sampled listings with an explicit closing time.
Current sampled listings with a usable posting date.
Current sampled listings that explicitly indicate confirmed, possible or unavailable sponsorship evidence.
Current sampled listings with an explicit relocation signal.
Low coverage means the current visible listings state the information less often. It does not prove that the employer lacks the policy, benefit or support.
Common current occupation families: Sales (8) · Product (7) · Data & AI (3)
Visible seniority mix: Senior / lead (24) · Leadership (13)
Current employment-type mix: Full time (39)
Most visible hiring countries in this sample: United States (39)
Other current roles from this employer
Client Strategy Manager
USA
Senior / lead · Region-restricted remoteAssociate Director of Product Development
USA
Leadership · Region-restricted remoteData Product Analyst, Corporate
USA
Senior / lead · Region-restricted remoteClient Strategy Manager - Corporate Brands & Retail
USA
Leadership · Region-restricted remoteThese are other current vacancies visible from the same employer, not endorsements or evidence of future hiring plans.
Current disclosed salary medians by currency
- USD 157,500 median · 38 disclosed salaries
Questions worth asking this employer
- If work authorization matters to you, what sponsorship policy applies to this specific role and location?
- If a move is required, is relocation assistance available for this position?
- What is the expected application and interview timeline if the listing does not state a closing date?
These questions are prompted by missing or thin evidence in the employer’s current visible vacancies. Missing evidence is not treated as a negative judgement.
This employer snapshot uses the employer’s current visible listings. Remote and sponsorship rates use only vacancies with explicit evidence, while missing information stays unknown. Salary medians are shown separately by currency and need at least five disclosed salaries. Zero confirmed sponsorship means none was explicitly found in this sample, not that sponsorship is unavailable.
This snapshot describes the employer’s current visible vacancies—things such as pay disclosure and work arrangement. It is not an employee-satisfaction or hiring-quality rating, and different currencies stay separate.
Work-location intelligence
Remote Freedom
Remote with some uncertainty or restrictions
Why this score?
- The role is remote but the source identifies a country or regional restriction.
Location restrictions detected
- Geographic eligibility is restricted to a country or region.
Remote Freedom measures observable location flexibility only. Unknown work-location information remains unscored. Timezone wording is penalized only when it appears restrictive; general collaboration across time zones is not treated as a restriction. A score does not guarantee legal work eligibility, payroll availability or visa sponsorship.
International applicant intelligence
Visa & relocation signals
Sponsorship status is shown only when the listing provides clear enough wording. Employer history or headquarters location is not used to guess an answer.
The available listing details do not contain a clear enough sponsorship statement.
The available listing details do not contain a clear enough relocation statement.
Tradeoff check
What’s the catch?
These are visible conditions worth checking before treating the headline compensation as the whole story:
- The listing appears to require substantial prior experience.
- This is remote work with a geographic restriction, not a worldwide-remote vacancy.
Application-path intelligence
What can we see about the application process?
Provider-mediated application path
The current application path comes through a publisher or aggregator. The destination may still lead to an employer-authorized hiring system, but that is not clear enough to assume.
Current destination: jobicy.com · HTTPS
No additional required application step is clear in the available listing text. This does not mean the external application is one-click or friction-free.
Application effort is estimated from visible listing and source details. Optional steps are kept separate from mandatory ones, and the external form may contain additional requirements that are not visible here.
Application safety context
Application safety check
Basic application-safety context available
The current application destination has a basic transport-safety signal such as HTTPS. This does not verify the employer, vacancy, recruiter or application process.
Evidence checked: Source provenance · Application destination · Listing wording
Observable transport-safety signals
- The available application destination uses HTTPS.
Before you share money or sensitive information
- Confirm the vacancy on the employer’s official careers site when practical, especially if a recruiter contacts you outside the listed application path.
- Check that recruiter email domains and application destinations are consistent with the employer or an identifiable recruiting provider.
- Do not pay application, processing, training or equipment fees merely to be considered for a job.
- Do not send cryptocurrency, gift cards, online-banking credentials or PINs as part of an application.
- Treat unusually fast offers or interviews conducted only through informal messaging channels as reasons to verify the employer independently.
This is a limited safety screen, not employer verification and not a fraud determination. Never send money, cryptocurrency, passwords or sensitive banking credentials merely because a listing appears on a job platform.
Original listing details
The section below comes from the listing source. The decision sections above use the job’s structured pay, location, qualification, mobility and application details to give you additional context.
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.
Listing provenance
Where did this job come from?
See where this vacancy came from and whether more than one current source can still support it. Source names help with traceability and attribution; they are not endorsements or guarantees.
At least one enabled source recently observed the vacancy. The source chain is traceable, but this is not independent employer verification.
- Continuity
- One recent renewable source
- Source-class independence
- One renewable source class
- Last source observation
- Observed very recently
This records when High Paid Job last observed the vacancy in source evidence. It confirms source presence, not that the employer edited the vacancy at that time.
- 1 enabled source recently observed this vacancy.
Source confidence is about traceability and continuity. It is separate from application safety and should not be read as an employer reputation rating.
The vacancy is supplied by an external job publisher. Source information helps show where the listing came from; it is not a guarantee about the employer or vacancy.
Listing source: Jobicy. Destination: jobicy.com. The application opens in a new tab so you can keep this report open.
