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Salary: USD 148,000 - 185,000 / annual
Recharge is the subscription platform for the world's fastest-growing brands, serving over 20,000 merchants globally and processing data from 100+ million shoppers. As a Senior Data Engineer, you will be a core member of the centralized Data and Analytics team, responsible for building scalable data infrastructure and pipelines that power both internal business analytics and customer-facing data products.
Your primary responsibilities include designing and implementing robust data pipelines, ELT solutions, and warehouse infrastructure that enable analysts to derive strategic insights and merchants to access real-time metrics. You will create automated monitoring, auditing, and alerting systems to ensure data quality and consistency across the platform. Working cross-functionally with business, application, and solution teams, you'll implement data strategies, build data flows, and develop comprehensive data models (conceptual, logical, and physical).
A key focus area is building next-generation analytics capabilities by delivering trusted data and metrics through AI/LLM tools (such as Claude), including a governed semantic layer and MCP-based interfaces for both internal and external users. You will continuously optimize data warehouse operations, monitoring, and performance, while also redesigning existing processes using AI-assisted development, automated documentation, and intelligent issue resolution to enhance team productivity.
You'll influence stakeholders across all levels—analysts, developers, business users, and executives—communicating complex data concepts clearly and championing Recharge's core values of ownership, empathy, and humility.
Required experience includes 5+ years in data engineering roles (Data Engineer, Data Platform Engineer, Analytics Engineer) with a proven track record of building scalable solutions. You need 3+ years of hands-on experience designing data pipelines and models for ingesting, transforming, and delivering large volumes of data from multiple sources into dimensional data warehouses or data lakes. You should be proficient with modern data platforms (Snowflake, BigQuery, MySQL, Postgres, RDS, AWS, GCP) and experienced in building pipelines powering external, customer-facing analytics applications. Strong knowledge of data warehousing methodologies (Kimball, Inmon), ETL tools (Fivetran, dbt, Python), and workflow orchestration (Airflow, Cloud Composer) is essential. You'll need hands-on experience with data infrastructure tools like Kubernetes and Docker, expert SQL proficiency, and strong Python skills.
