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Welcome to Chargeflow Chargeflow is building the operating system for chargeback management. We help merchants automatically fight and recover disputed payments, reducing revenue loss and operational overhead. Our platform processes large volumes of payment and dispute data across merchants, payment processors, and financial systems. As Chargeflow scales its platform and customer base, data becomes one of the company’s most strategic assets - powering better products, smarter decisions, and operational efficiency.
Who We're Looking For - The Dream Maker We are looking for a Head of Data Analytics to build and lead the company’s data platform and data strategy. The Head of Data will own Chargeflow’s entire data platform, research, and data strategy This includes building the data infrastructure that powers the company, establishing reliable company-wide metrics, enabling data-driven decision making, and turning Chargeflow’s dispute and payments data into a long-term product advantage. This is a foundational leadership role responsible for making data a core capability across the organization. The Head of Data Analytics will work closely with Product, Engineering, Operations, and Leadership to ensure data becomes a central part of how the company builds products and runs the business. ?Your Arena ?Data Platform and Architecture - Design and lead the development of Chargeflow’s modern data infrastructure:
* Building scalable data pipelines across product, payments, disputes, and internal systems
* Developing a centralized data warehouse as the company’s single source of truth
* Ensuring data quality, reliability, and observability
* Establishing data governance and best practices Company Metrics and Decision Intelligence - Define and standardize the core metrics used across the company:
* Establishing a consistent metrics framework across product, revenue, and operations
* Building dashboards used by leadership and operational teams
* Ensuring reliable and accessible reporting across the organization
* Supporting data-driven decision making at the leadership level Product Data and Insights - Partner with Product and Engineering to leverage Chargeflow’s unique dispute and payment data:
* Identifying patterns and insights across disputes and recovery performance
* Enabling data-driven product development
* Supporting predictive capabilities and automation in dispute management
* Helping build product features powered by data insights Operational Intelligence - Provide visibility and insights into dispute operations and business performance:
* Building operational analytics dashboards
* Supporting forecasting and capacity planning
* Identifying automation opportunities
* Improving operational performance through data insights Building the Data Organization - Build and lead the company’s data team:
* Hiring and managing data engineers, analytics engineers, and analysts
* Establishing strong data culture and best practices
* Enabling teams across the company to use data effectively
* Ensuring data privacy, security, and governance standards What Success Looks Like - Within the first 12–18 months:
* Chargeflow operates from a trusted company-wide data platform
* Leadership and teams rely on consistent and reliable metrics
* Product teams leverage dispute data to build data-powered features
* Operational performance becomes clearly measurable and optimized
* A strong Data organization is established and growing

We're in search of an exceptional hands-on Data Scientist who can bridge data infrastructure, fraud analytics, and machine learning to enhance our fraud detection capabilities. This role requires someone who can drive projects from ideation to implementation, leading these efforts while thinking strategically about how we detect and prevent fraud at scale. Our ultimate goal is to equip our clients with resilient safeguards against chargebacks, empowering them to safeguard their revenue and optimize their profitability. Join us on this thrilling mission to redefine the battle against fraud.

Your Arena

Develop and enhance fraud detection models from concept to production, focusing on data normalization, network analysis, and risk scoring.
Take ownership to identify, analyze, and implement solutions for fraud patterns and behavioral anomalies.
Design, build and maintain data science pipelines and fraud intelligence systems to improve detection accuracy.
Collaborate with product, engineering, and risk teams to implement fraud prevention strategies.
Own projects end-to-end - balancing short-term wins with long-term strategy.

We're looking for a Data Engineer with 4+ year experience to join our Data Engineering team and help us build and scale our production-grade data platform. You'll work on high-performance systems built on self-hosted ClickHouse, optimize complex data pipelines, and collaborate closely with Product, Analytics, and Infrastructure teams to deliver reliable, fast, and scalable data solutions.

This is a hands-on technical role where you'll have a significant impact on how we ingest, model, store, and serve data that powers our analytics and AI-driven products.
Youll play a key role in shaping the direction of our data platform and have meaningful ownership over critical components of our architecture.

What You'll Do:
Data Modeling & Architecture
Design and evolve data models that reflect business logic and support analytical use cases
Collaborate with the BI and Analytics teams to understand data requirements and translate them into efficient schemas
Performance Optimization
Optimize ClickHouse schemas, partitioning strategies, indexing, and compression
Profile and tune slow queries to improve performance and reduce costs
Implement systems that ensure data quality, consistency, and operational efficiency (e.g., deduplication, validation, anomaly detection)
Monitor pipeline health, data freshness, and query performance with appropriate alerting mechanisms
SQL Compiler Development
Develop and maintain the SQL Compiler layer that translates high-level queries into optimized ClickHouse execution plansImplement query optimization and rewriting strategies to improve performanceDebug and resolve compiler issues to ensure accurate and efficient query translation

Data Pipeline Development & Collaboration
Review and advise the Integration team on pipeline architecture, performance, and best practices.
Provide guidance on data modeling, schema design, and optimization for new data sources.
Troubleshoot and maintain existing pipelines when issues arise or optimization is needed
Ensure data freshness, reliability, and quality across all ingestion pipelines.
Collaboration & Support
Work closely with the Integration team to ensure smooth data ingestion from new sources.
Partner with Infrastructure to support high availability and disaster recovery
Support other teams across the company in accessing and using data effectively.