• Support Enginuity to unleash the power of sector data by providing actionable data analysis and market intelligence through the development of BI dashboards. • Collect, analyse and interpret data to provide insights on market trends, sector performance, workforce needs, productivity, skills gaps and emerging opportunities. • Analyse and monitor complex sector data within PowerBI, produce ad hoc data and reports. • Collaborate with teams across the organisation to deliver tailored insights. • Support the development of data solutions, ensure data quality and compliance standards are maintained. • Help integrate data from multiple sources. • Contribute to the development of AI capabilities and commercial data products.

• Build dashboards for clients to monitor their eStore. • Assist with project accounting by coordinating with respective project managers and generating reports and invoices for review. • Create data architectures, Stored Procedures, and Functions using programming languages. • Help prepare reports by collecting, analyzing, and summarizing account information and trends. • Audit to ensure accuracy of financial data. • Contribute to team effort by accomplishing related results as needed. • Protect the organization's value by keeping information confidential.

Role Description Launch Potato is looking for a Lead BI Developer to own the reporting infrastructure that powers decision-making across our Credit Cards, Make Money/Manage Money, Banking, and Marketplace verticals with a clear growth path into becoming a proactive analytical partner who helps the team see around corners. You'll be embedded with the business from day one, partnering closely with our Yield, Paid Media, and Account Management teams to: - Uncover meaningful trends - Conduct retrospective analyses across experiments, creatives, offers, seasonality, and CAC performance - Challenge the team with insights they aren't currently seeing This is NOT a role for someone who wants to build dashboards and hand them off; it's for someone who cares deeply about whether the work they build gets used, understood, and acted on. Outcomes (Performance Expectations) - 30 Days in: Actively engaged with stakeholders to audit what reporting the team is consuming and why, identifying discrepancies, redundancies, and inconsistencies across existing dashboards and beginning to develop a roadmap to standardize reporting across your verticals. - 60 Days in: Bridging business context and data understanding by articulating how assigned verticals make money and what the key performance levers are. Actively improving dashboard standardization while executing on ad hoc requests with accuracy and business context. - 90 Days in: Dashboard standardization is in execution and a clear roadmap exists for how trusted data sources will be developed to improve the efficiency of data consumption. LookML structure is understood well enough to begin making changes with full awareness of downstream impacts. Working relationships with Yield, Paid Media, and Account Management are established. - 6 Months in: Trust is built with the business team through education, best practices, and a standardized reporting suite that enables confident self-service analytics. Proactive retrospective analyses are being delivered across experiments, creatives, offers, seasonality, and CAC performance. Strong understanding of the full funnel metrics that support each team. - Year 1: Trusted technical owner for assigned verticals end-to-end, from raw data ingestion through final dashboard. Reporting standardization is complete, retrospective analysis is a consistent output, and the business team views you as a go-to analytical partner who recommends action, not just reports findings. Qualifications - Minimum 7 years in business intelligence or analytics with direct exposure to performance or affiliate marketing. - Experience owning reporting end-to-end, from pipeline to dashboard. - Ability to work with messy, complex, third-party data. - Experience partnering closely with business teams to uncover meaningful trends and conduct retrospective analyses. Requirements - Expert SQL & Comfortable with Unstructured Complex Data: Extracts accurate insights from raw, unstructured data without relying on clean models. - Reporting Ownership & Data Literacy: Builds, standardizes, drives adoption, and leaves teams more data-literate. - Performance Marketing Optimization: Connects data directly to levers that move the business, i.e. campaign performance, conversion rates, or revenue per session. - Advanced LookML Development (or equivalent in Power BI / Tableau): Architects complex models with full understanding of downstream impacts. - Curiosity & Business Depth: Asks questions first; driven to grow business knowledge to become a trusted analytical partner, not just a builder. Benefits - Base salary: $120,000 to $150,000 per year, paid semi-monthly. - Compensation package includes a base salary, profit-sharing bonus, and competitive benefits. - Performance-driven company: Future increases based on company and personal performance, not annual cost of living adjustments.

• Act as and innovate as an internal analytics consultancy and data ambassador within the CRM team and Marketing leadership, using technical data analysis skills and business knowledge to drive discussions about customer behavior and provide recommendations • Lead the definition and consolidation of measurement models for loyalty initiatives, establishing methodologies, assumptions and governance for strategic KPIs • Develop and evolve uplift/incremental evaluation models for cashback strategies, benefits and loyalty incentives • Ensure consistency of the program’s performance indicators, supporting the creation of a single view of results for business stakeholders • Serve as an analytics consultant to Marketing, CRM and Loyalty teams, turning data into actionable recommendations • Produce analyses that support decisions regarding the expansion, optimization or review of customer benefits • Conduct studies on behavior and the evolution of the customer base participating in the loyalty program, identifying impacts on retention, purchase frequency, average order value (AOV) and customer lifetime value (CLV) • Support the transition of the loyalty initiative to a continuous operational model, ensuring that measurement models are documented, scalable and governed