Recharge

IT Support

• Live by and champions all of our core values (#accountability, #collaboration, #iteration, and #details)
• Lead a high-caliber engineering team that will scale as the business grows, identifying and filling any team gaps
• Perform personnel management tasks for a team of engineers, including performance reviews, compliance with company policies, mentoring, and helping plan career growth
• Understand and manage team morale through regular assessments and collaboration with engineering leadership
• Train new engineers on our best practices, standards, culture, and policies
• Foster a team-oriented, supportive culture with a strong focus on creating a working environment that fosters collaboration, inclusion, open communication, and delivering outcomes using agile methodologies
• Work with all departments regarding general employee empowerment and productivity
• Be responsible for quality team output, on-call rotation and maintenance schedule, and overall project management
• Own IT tooling management including vendor relationships and procurement management
• Constantly improve team productivity, refine agile processes, and create methods to increase efficiency
• Investigate, analyze, and evangelize good engineering practices
• Lead in the creation and execution of novel solutions to complex, unbounded technical problems. Expertly design and own technical solutions for broad or complex requirements with insightful and strategic approaches.
• Align your team’s work with Recharge’s business and engineering priorities to realize meaningful impact. Identify and build support for technical initiatives that address Recharge’s most important needs
• Collaborate with the Director of Security in setting roadmap and priorities to ensure deliverables are met as well as raising concerns identified early on.

IT Manager

• Lead a high-caliber engineering team that will scale as the business grows, identifying and filling any team gaps
• Perform personnel management tasks for a team of engineers, including performance reviews, compliance with company policies, mentoring, and helping plan career growth
• Understand and manage team morale through regular assessments and collaboration with engineering leadership
• Train new engineers on our best practices, standards, culture, and policies
• Foster a team-oriented, supportive culture with a strong focus on creating a working environment that fosters collaboration, inclusion, open communication, and delivering outcomes using agile methodologies
• Be responsible for quality team output, on-call rotation and maintenance schedule, and overall project management
• Own IT tooling management including vendor relationships and procurement management
• Constantly improve team productivity, refine agile processes, and create methods to increase efficiency
• Investigate, analyze, and evangelize good engineering practices
• Lead in the creation and execution of novel solutions to complex, unbounded technical problems

Data Engineer

• Build data pipeline, ELT and infrastructure solutions to power internal data analytics/science and external, customer-facing data products.
• Create automated monitoring, auditing and alerting processes that ensure data quality and consistency.
• Work with business and application/solution teams to implement data strategies, build data flows, and develop conceptual/logical/physical data models
• Design, develop, implement, and optimize existing ETL processes that merge data from disparate sources for consumption by data analysts, business owners, and customers
• Seek ways to continually improve the operations, monitoring and performance of the data warehouse
• Influence and communicate with all levels of stakeholders including analysts, developers, business users, and executives.
• Redesign and optimize existing data processes using AI (e.g., LLM-assisted development, automated documentation, issue resolution) to improve team productivity and enhance data quality across the data warehouse
• Live by and champion our values: #day-one, #ownership, #empathy, #humility.

Senior Data Engineer

• Build data pipeline, ELT and infrastructure solutions
• Create automated monitoring, auditing and alerting processes
• Work with business and application teams to implement data strategies
• Design, develop, implement, and optimize ETL processes
• Improve operations, monitoring and performance of data warehouse
• Influence and communicate with stakeholders
• Redesign and optimize existing data processes using AI
• Live by and champion company values

Engineering Manager

• Directly manage 6 engineers across two squads with distinct technical profiles
• Stay engaged in MRs, technical decisions and day-to-day engineering quality
• Actively unblock engineers - technically and organizationally
• Partner cross-functionally with product and stakeholders while protecting team focus and quality
• Experiment with and help establish AI tooling practices that scale your team’s output

Engineering Manager, Merchant Experience

Data Analyst

• Live by and champion all of our core values (#ownership, #empathy, #day-one, and #humility).
• Build and maintain durable, documented, tested models in dbt that power GTM reporting (e.g., pipeline, funnel, conversion, bookings, forecasting, rep performance, account health).
• Design dimensional models (facts/dimensions), metrics definitions, and “one version of the truth” datasets to make analysis repeatable and trustworthy.
• Develop and maintain Looker explores, dashboards, and standardized reporting that enable self-serve by the GTM organization.
• Implement data quality checks, monitoring, and model documentation so stakeholders can confidently act on results.
• Partner with data engineering on upstream improvements (instrumentation, source system hygiene, pipeline reliability).
• Collaborate with GTM stakeholders to clarify business questions and translate them into analysis plans, metrics, and decision frameworks.
• Perform analysis using SQL (and Python as needed) to identify trends, diagnose performance, and recommend actions (e.g., funnel bottlenecks, win-rate drivers, deal velocity, territory/segment opportunities).
• Build GTM narratives that connect data to decisions—clearly communicating insights with business context to leaders and cross-functional partners.
• Proactively monitor performance and adoption signals to surface risks, anomalies, and opportunities—then drive stakeholders toward resolution.
• Identify high-leverage GTM “insight bottlenecks” and design data products to solve them (e.g., standardized KPI hubs, automated pipeline risk alerts, self-serve funnels, guided explorations, executive views).
• Leverage LLMs/AI to accelerate insight workflows—examples might include: AI-assisted “metric explainers” embedded into dashboards, natural-language Q&A experiences over curated GTM datasets, automated insight summaries / anomaly narratives for weekly GTM reviews, guided self-serve templates (“answer this question in 3 clicks”) for common GTM use cases.
• Establish success metrics for data products (e.g., reduced time-to-insight, increased self-serve usage, fewer ad-hoc requests, improved forecast accuracy).
• Work independently in ambiguous environments—framing problems, generating hypotheses, and leading toward outcomes.
• Manage multiple priorities and communicate clearly through Slack, docs, and live stakeholder readouts.
• Contribute to team standards (modeling patterns, documentation, code review, stakeholder intake).
• Mentor other analysts through collaboration, query/design feedback, and best practices (where applicable to your team structure).

Senior Data Analyst, GTM Analytics

• Live by and champion all of our core values (#ownership, #empathy, #day-one, and #humility).
• Build and maintain durable, documented, tested models in dbt that power GTM reporting (e.g., pipeline, funnel, conversion, bookings, forecasting, rep performance, account health).
• Design dimensional models (facts/dimensions), metrics definitions, and “one version of the truth” datasets to make analysis repeatable and trustworthy.
• Develop and maintain Looker explores, dashboards, and standardized reporting that enable self-serve by the GTM organization.
• Implement data quality checks, monitoring, and model documentation so stakeholders can confidently act on results.
• Partner with data engineering on upstream improvements (instrumentation, source system hygiene, pipeline reliability).
• Collaborate with GTM stakeholders to clarify business questions and translate them into analysis plans, metrics, and decision frameworks.
• Perform analysis using SQL (and Python as needed) to identify trends, diagnose performance, and recommend actions (e.g., funnel bottlenecks, win-rate drivers, deal velocity, territory/segment opportunities).
• Build GTM narratives that connect data to decisions—clearly communicating insights with business context to leaders and cross-functional partners.
• Proactively monitor performance and adoption signals to surface risks, anomalies, and opportunities—then drive stakeholders toward resolution.
• Identify high-leverage GTM “insight bottlenecks” and design data products to solve them (e.g., standardized KPI hubs, automated pipeline risk alerts, self-serve funnels, guided explorations, executive views).
• Leverage LLMs/AI to accelerate insight workflows—examples might include:
• AI-assisted “metric explainers” embedded into dashboards
• Natural-language Q&A experiences over curated GTM datasets
• Automated insight summaries / anomaly narratives for weekly GTM reviews
• Guided self-serve templates (“answer this question in 3 clicks”) for common GTM use cases.
• Establish success metrics for data products (e.g., reduced time-to-insight, increased self-serve usage, fewer ad-hoc requests, improved forecast accuracy).
• Work independently in ambiguous environments—framing problems, generating hypotheses, and leading toward outcomes.
• Manage multiple priorities and communicate clearly through Slack, docs, and live stakeholder readouts.
• Contribute to team standards (modeling patterns, documentation, code review, stakeholder intake).
• Mentor other analysts through collaboration, query/design feedback, and best practices (where applicable to your team structure).