Data Analyst

Role: Data Analyst, BusinessLoans.com

Location: Charleston, SC (must be located in Greater Charleston Area)

Business Information

BusinessLoans.com is a leading small business financing platform focused on making small-business financing simpler, smarter, and more accessible. We help entrepreneurs secure the capital they need to start, operate, and grow by combining data, intelligent matching, and a curated lender network.

We are a fast-growing company solving meaningful problems at scale. Our teams work closely across product, analytics, marketing, and sales to build solutions that directly impact millions of small businesses. The work is high-visibility, collaborative, and ownership-driven.

As we continue to scale, data and analytics are central to how we drive performance across marketing, sales, marketplace distribution, and renewals, and to how we build the next generation of AI-enabled products.

Role Overview

This is a hands-on individual-contributor role on a growing analytics team that supports the entire revenue organization, including marketing, direct sales, marketplace, and renewals.

You will spend your time close to the business: answering the questions leaders are actually asking, building the reporting they run on, and digging into the "why" behind conversion, lead quality, and revenue performance. You will also spend real time on the foundations; writing and reviewing the SQL that defines our metrics, moving business logic out of dashboards and into the warehouse, and helping build the trusted, self-serve reporting layer the company is investing in.

We are looking for someone with a few years of analytics under their belt who is ready to own a domain. You should be comfortable communicating with business stakeholders, unpacking and scoping data requests, and coming back with a defensible answer.

You will work directly with senior stakeholders across the business, and your work will be visible to them. This is a strong role for an analyst who wants exposure to the full revenue picture of a fintech marketplace and the chance to help build the data foundation rather than inherit a finished one.

Responsibilities

Reporting & Self-Serve Analytics

• Build, maintain, and improve the dashboards and reporting that marketing, sales, marketplace, and renewals leaders use to run their business day to day.
• Own a portfolio of reports end to end, not as one-time handoffs, but as living products you monitor, iterate on, and retire when needed.
• Help move the business toward self-service: build well-documented, well-named datasets and metrics so stakeholders can answer their own routine questions.
• Proactively surface anomalies and performance shifts to stakeholders rather than waiting for them to be discovered in a monthly review.

Funnel, Revenue & Marketing Analysis

• Use CRM data to analyze the lifecycle of a lead throughout the sales process
• Support marketing and channel performance analysis: lead quality by source and campaign, cost and revenue per lead, and attribution across paid, organic, affiliate, and partner channels.
• Support marketplace and lender performance analysis: routing, match and offer rates, kick reasons, pricing, and partner-level economics.
• Run root-cause investigations when a metric moves unexpectedly, and translate what you find into a clear recommendation, not just a chart.
• Deliver ad hoc analysis for senior stakeholders under real time pressure, while being honest about what the data can and cannot support.

Data Quality, Definitions & Trust

• Investigate and resolve discrepancies between reports, and document the root cause.
• Contribute to a shared catalog of metric definitions and business logic, writing the definition down, agreeing it with the business owner, and implementing it consistently.
• Apply and help maintain the data-quality checks that keep reporting trustworthy, including the known traps in our data (test and duplicate leads, nullable flags, clawbacks, and revenue-basis changes over time).
• Flag data-integrity and pipeline issues to engineering with enough diagnosis attached to be actionable.

Data Modelling & Warehouse Contribution

• Write clean, performant, reviewable SQL against a PostgreSQL warehouse.
• Help move transformation and metric logic out of the BI layer and into governed, version-controlled models in the warehouse, working alongside engineering and the wider data team.
• Contribute to a layered warehouse structure: staging models, curated business tables, and the documented metrics built on top of them.
• Build reusable models rather than one-off queries wherever the same logic is likely to be asked for twice.

Stakeholder Partnership & Ways of Working

• Partner directly with marketing, sales, marketplace, renewals, finance, and product stakeholders, clarifying vague requests into scoped, prioritized work.
• Work through the team's request intake and prioritization process, keeping your work visible and your commitments realistic.
• Communicate findings clearly to non-technical audiences, in writing and live, with the caveats stated up front rather than buried.
• Document what you build so it survives you being on vacation.

AI-Assisted Analysis

• Use AI tooling (i.e. Claude, Gemini) to accelerate query writing, exploration, documentation, and analysis, with strong judgment about where/when it helps and where it needs checking.
• Help make our AI-facing data assets genuinely useful: clear table and field descriptions, governed and PII-safe views, and definitions precise enough that a model can use them correctly.
• Apply our guardrails for handling PII and sensitive financial data when working with AI tools.

Requirements

Background & Experience

• 1-4 years of experience in a data analyst, business intelligence, marketing analytics, or revenue analytics role
• Demonstrated experience owning reporting or analysis for a business function, not just executing assigned tickets
• Experience working with Customer or Ops data in a commercial context
• A track record of analysis that led to a decision or a measurable change

Skills & Capabilities

• Strong SQL - Comfortable with joins, window functions, CTEs, and aggregation, and able to write queries other people can read and review
• Hands-on experience with BI tools - Tableau specifically is a strong plus; experience with Metabase, Looker or similar is equally welcome
• Solid data-modelling fundamentals - Understanding of grain, fan-out, fact vs. dimension, and why a poorly chosen join inflates a count
• Details focused and an eye for quality - The instinct to sanity-check a number before it goes out, and the confidence to challenge one that looks wrong
• Strong communication - Clear written and verbal communication with non-technical stakeholders
• Time management - Ability to manage competing requests from multiple stakeholders and be transparent about trade-offs
• Comfort with ambiguity - Taking a loosely specified question and scoping it yourself.
• Hands-on with AI - Hands-on experience with Generative AI tools (Claude, OpenAI, or similar) and sound judgment about using them responsibly on sensitive data

Preferred, but not required

• Experience in fintech, lending, or financial services, or in a marketplace or performance-marketing environment
• Familiarity with marketing analytics concepts: attribution models, UTM and channel taxonomy, CAC/CPL, and lead-quality measurement
• Exposure to dbt (or a similar transformation framework), Git, and version-controlled analytics workflows
• Working knowledge of Python for analysis or automation is a plus
• Experience contributing to metric definitions, data documentation, or a semantic layer
• Experience working alongside a data engineering function

Compensation & Benefits

Salary: $65,000 to $95,000 depending on experience

Comprehensive benefits:

• Health, dental, and vision coverage
• Flexible PTO plus company-wide recharge days
• Paid holidays
• Short- and long-term disability coverage
• 401(k)