Data Intelligence and Reporting Lead

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.