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Overview

The Data Intelligence and Reporting Lead will be responsible for ensuring data reliability, structuring reporting frameworks, standardizing signals, and providing actionable insights in a dynamic tech and operations setting. The role requires transforming complex operational data into dependable reports, well-defined metrics, predictive indicators, and automated analytical workflows to enable faster, informed decisions across teams.

Responsibilities

Maintain rigorous data integrity by cleaning, validating, and normalizing business data to enhance reliability.
Spot and address duplication, inconsistencies, or weak data points before they impact decision-making.
Define and enforce uniform standards for tags, signals, statuses, categories, and operational events to ensure consistency across teams.
Develop statistical frameworks for A/B testing, including sample size calculation, confidence assessment, and clear decision criteria; educate teams on interpreting test outcomes.
Create efficient, repeatable reporting architectures such as dashboards and standardized data packets to reduce manual reporting through automation.
Identify trends, risk signals, and future opportunities from operational data to shift the focus from historical reporting to predictive analytics.
Produce aggregated and anonymized insights to protect privacy while illuminating strategic commercial data opportunities.
Collaborate cross-functionally with technical, operations, finance, growth, and leadership teams to ensure reporting tools effectively inform real decisions.
Challenge and refine unclear data assumptions, inconsistent labeling, weak dashboards, and unsupported conclusions.
Utilize large language model (LLM) tools responsibly to accelerate data analysis, reporting, summaries, and documentation while maintaining accuracy and governance.

Key Deliverables

Oversee comprehensive data quality reviews for reports, dashboards, tags, signals, and key performance metrics.
Develop and sustain a detailed data dictionary encompassing vital metrics, tags, statuses, and reporting elements.
Construct and support BI dashboards, automated reports, and repeatable reporting workflows.
Convert raw operational data into structured reporting artifacts accessible by leadership and operational teams.
Standardize signal and tag definitions across various teams and functions.
Support predictive analytics by identifying patterns and leading indicators.
Design, implement, and guide A/B testing protocols.
Ensure anonymization and aggregation of data to safeguard privacy.

Required Competencies

Expertise in data analysis, including cleansing, comparison, and interpretation of operational and commercial datasets.
Advanced proficiency in spreadsheets (Excel or Google Sheets) including formulas, pivot tables, data cleaning, and structured reporting.
Experience with business intelligence tools and dashboard/report platforms to design meaningful reports.
Capability to query data using SQL or effectively collaborate with technical teams for accurate data retrieval and validation.
Solid understanding of statistics, including A/B testing concepts such as sample size, confidence, variance, significance, and experimental design.
Strong data governance skills, including metric definition, data dictionary management, and maintaining a reliable source of truth.
An automation-oriented mindset to streamline reporting processes while preserving critical human review points.
Comfortable utilizing AI/LLM technologies to expedite analysis and documentation while ensuring accuracy and oversight.
Excellent communication skills to clearly convey findings to both technical and non-technical stakeholders.

Preferred Experience

Background in fintech, insuretech, SaaS, marketplaces, revenue operations, startup operations, customer intelligence, analytics, growth operations, or tech-enabled services.
Experience in high-velocity startup or technology-driven environments.
Familiarity with SQL, Python, R programming, ETL/data pipelines, data warehouses, CRM systems, BI platforms, or API data integration.
Hands-on experience with A/B tests, funnel optimizations, campaign testing, or conversion rate analysis.
Knowledge of data privacy best practices, anonymization techniques, metric dictionary ownership, and QA of reporting outputs.

Work Approach and Mindset

Agile and responsive under tight deadlines with clear communication.
Inquisitive and critical thinker who questions assumptions and verifies data integrity.
Structured problem solver who translates ambiguous requests into concrete definitions, reports, experiments, and actionable reports.
Adopts modern AI and automation tools strategically to boost productivity while safeguarding data accuracy and governance standards.
Confident yet respectful in challenging data and interpretations collaboratively.
Business-oriented mindset recognizing that data analytics must support decisions, revenue growth, operations, and customer success.

Clarifications on Role Scope

This position is not focused on manual data entry or spreadsheet updating.
Accounting expertise is not required though the role supports financial reporting simplification.
The role is broader than solely dashboard creation; it encompasses data definitions, source quality, automation logic, and enabling decision-making readiness.
Excel skills are valuable but must be complemented by knowledge in BI tools, automation, data governance, and statistical analysis.
Programming skills are beneficial but core responsibilities do not center on development or writing production code.
The candidate must proactively challenge poor data quality, missing definitions, and unsupported conclusions rather than passively analyzing.

Success Milestones Within 90 Days

First 30 Days: Acquire understanding of existing data sources, reports, tags, Excel workflows, dashboards, gaps, and pain points; perform initial data audit with a focus on quick automation wins.
First 60 Days: Establish foundational data dictionaries, key performance indicator definitions, signal/tag taxonomies, and initial reporting frameworks; enhance recurring reports and experimental designs.
First 90 Days: Implement data quality checks, increase reporting efficiency, standardize metrics, and enable teams to adopt data-driven decision-making, forecasting, and experimentation.

Data Intelligence and Reporting Lead