Data Intelligence & Reporting Lead

Contract, Full Time
Remote
Posted 2 weeks ago
About the Role We are hiring a Data Intelligence & Reporting Lead to own data quality, reporting structure, signal standardization and decision-ready intelligence across a fast-moving technology and operations environment. This role is for someone who can turn messy operational data into trusted reporting, governed metrics, useful signals, predictive insights and automated workflows. The ideal candidate is not just a report builder; they can challenge data, define what metrics mean and help teams make better decisions faster. What You Will Own Data Integrity
  • Clean, validate, normalize and improve the reliability of business data.
  • Identify duplicated, inconsistent or weak data before it affects decisions.
Signal & Tag Standardization
  • Create consistent definitions for tags, signals, statuses, categories and operational events.
  • Prevent teams from using different labels for the same concept.
A/B Testing & Experimentation
  • Support statistically valid tests with sample size, confidence and clear decision rules.
  • Help teams understand when results are meaningful and when they are not.
Reporting Architecture
  • Design repeatable reporting structures, dashboards and report-ready data packets.
  • Help teams reduce manual reporting work through automation.
Predictive Intelligence
  • Identify patterns, trends, risk indicators and future opportunity signals from operational data.
  • Move reporting from hindsight toward decision support.
Anonymized Insight Packaging
  • Prepare aggregated and anonymized insight outputs where appropriate.
  • Protect privacy while helping the company understand commercial data opportunities.
Key Responsibilities
  • Own data quality review across reports, dashboards, tags, signals and business metrics.
  • Create and maintain a data dictionary for key metrics, tags, statuses and reporting fields.
  • Build or help design BI dashboards, automated reports and repeatable reporting workflows.
  • Translate raw operational data into structured reporting packets that leadership and teams can use.
  • Standardize signal and tag definitions across teams, tools, campaigns, calls, customer intelligence and market activity.
  • Support predictive analytics efforts by identifying patterns, leading indicators and future opportunity signals.
  • Design A/B testing frameworks that include hypothesis, variants, sample size, confidence, timeline and decision criteria.
  • Help anonymize and aggregate data for safe insight development while protecting customer and operational privacy.
  • Use LLM tools responsibly to speed up analysis, summaries, reporting and documentation without losing accuracy or governance.
  • Challenge unclear data assumptions, inconsistent labels, weak dashboards and unsupported conclusions.
  • Work with technical, operations, finance, growth and leadership teams to ensure reporting supports real decisions.
Required Skills
  • Data analysis – Strong ability to clean, analyze, compare and explain operational and commercial data.
  • Advanced spreadsheets – High proficiency with Excel / Google Sheets, formulas, pivots, data cleaning and structured reporting.
  • BI and dashboards – Experience with BI tools or dashboard/reporting platforms; ability to define useful reporting views.
  • SQL or data querying – Ability to query data or work closely with technical teams to retrieve and validate source data.
  • Statistics and A/B testing – Understanding of sample size, confidence, variance, statistical significance and experiment design.
  • Data governance – Ability to define metrics, manage data dictionaries and protect source-of-truth discipline.
  • Automation mindset – Can identify reporting steps that should be automated and approval steps that should remain human-reviewed.
  • LLM-assisted work – Comfortable using AI/LLM tools to accelerate analysis, documentation, summaries and QA without blindly trusting output.
  • Communication – Can explain findings clearly to non-technical and technical stakeholders.
Preferred Background The strongest candidates may come from fintech, insuretech, SaaS, marketplace, revenue operations, startup operations, customer intelligence, analytics, growth operations or technology-enabled service environments. Strong Advantage
  • Fintech or insuretech data experience.
  • Startup or high-speed technology environment.
  • Revenue operations, marketplace or operational analytics.
Technical Advantage
  • SQL, Python or R exposure.
  • ETL / data pipeline awareness.
  • Data warehouse, CRM, BI or API data experience.
Experimentation Advantage
  • A/B testing, funnel optimization, campaign testing or conversion analysis.
  • Ability to communicate statistical confidence simply.
Governance Advantage
  • Data privacy, anonymization, source-of-truth design, metric dictionary ownership or reporting QA.
Mindset We Are Looking For
  • Fast and responsive – Moves quickly, communicates clearly and can work under tight timelines.
  • Curious and skeptical – Questions weak assumptions, checks the data and looks for what is missing.
  • Structured – Turns unclear requests into definitions, fields, reports, experiments and action-ready summaries.
  • Modern and LLM-first – Uses AI and automation as force multipliers while protecting accuracy and governance.
  • Confident but humble – Can challenge data respectfully without acting like the only expert in the room.
  • Business-aware – Understands that data exists to support decisions, revenue, operations and customer outcomes.
What This Role Is Not
  • Not Data Entry – This is not a role for manually updating spreadsheets or copying numbers between systems.
  • Not an Accountant – The person does not need to be an accountant, though they must structure data so finance/reporting becomes easier.
  • Not Dashboard-Only – Dashboards are outputs. The role owns definitions, source quality, automation logic and decision readiness.
  • Not Excel-Only Reporting – Advanced Excel is useful, but the role requires modern BI, automation, data governance and statistics.
  • Not a Pure Developer – Developer awareness is useful, but the role is not hired to write production code as the core responsibility.
  • Not Passive Analysis – The person must challenge unclear data, missing definitions and unsupported conclusions.
Success in the First 90 Days
  • First 30 Days
Understand current data sources, reports, tags, Excel workflows, dashboards, gaps and manual reporting pain points. Produce an initial data audit and quick-win automation list.
  • First 60 Days
Create a working data dictionary, core KPI definitions, signal/tag taxonomy and initial reporting architecture. Begin improving recurring reports and experiment design.
  • First 90 Days
Operationalize data quality checks, improve reporting speed, standardize core metrics and help teams use data for decisions, prediction and experiments. How To Apply Email your updated resume to hrteam@thefvg.com along with your responses to the following questions:
  • What are your hourly salary expectations in Canadian Dollars?
  • Have you previously worked during North American hours?
  • Are you willing to work during North American hours?
  • Are you comfortable with monthly pay?
  • Please share your educational background and any relevant certifications or professional designations.
Subject: Data Intelligence & Reporting Lead – [Your name] If you’re excited about building platforms that scale—not just features—we’d love to meet you.

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