Staff Data Engineer

FastSpring is looking for a Staff Data Engineer to serve as the technical architect and driving force behind our next-generation data platform. As a key partner to the Senior Data Product Manager, you will build the high-performance infrastructure required to turn hundreds of millions of rows of transactional data into actionable insights for our sellers.

This role is a high-impact technical leadership position responsible for designing and executing a modern data warehouse strategy that supports real-time product innovation. You will bridge the gap between complex transactional systems and the analytical needs of our global customers.

This role reports to the Senior Director, Development, and works cross-functionally with Product, Engineering, and Executive leadership to identify growth opportunities and execute technical go-to-market strategies.

• →Architect and Implement: Lead the design and construction of a next-generation data warehouse optimized for improved efficiency, performance, and massive scale.
• →Real-Time Data Ingestion: Build and maintain robust data pipelines for real-time ingestion from large-scale production transactional databases containing hundreds of millions of rows.
• →Stream Processing: Develop real-time data transformation layers to ensure the data warehouse reflects live business activity, enabling "up-to-the-minute" analytics for our ecosystem.
• →Technical Roadmap: Partner with the Senior Data Product Manager to define, evangelize, and execute a data roadmap that meets both internal and external customer needs.
• →Technical Leadership: Research and select appropriate technical solutions, coordinate delivery with dedicated agile teams, and champion engineering best practices.
• →Validation & Metrics: Rigorously test and validate data integrity, establishing and measuring success metrics for system performance and data reliability.
• →Collaboration: Work closely with stakeholders to develop technical business cases for new initiatives, ensuring alignment across the development organization.

• Experience: 5+ years of data engineering experience in a high-growth SaaS environment.
• Real-Time Messaging: Expert-level experience with message brokers and streaming platforms such as Apache Kafka, Amazon Kinesis, or Google Pub/Sub to manage high-throughput transactional data.
• Data Transformation: Proficiency with real-time and batch transformation tools, specifically dbt (data build tool) for managing Snowflake logic and Apache Flink or Spark Streaming for in-flight processing.
• Snowflake Ecosystem: Deep architectural knowledge of Snowflake, including experience with Snowpipe for continuous ingestion, streams, and tasks for change data capture (CDC).
• Database Internals: Strong understanding of schemas and data pipelines, with specific experience extracting data from large-scale relational databases (Postgres/MySQL) via CDC tools like Debezium.
• Technical Proficiency: Mastery of SQL and Python for building automated, scalable data movement and transformation workflows.
• Data APIs: Proven experience designing and maintaining Data APIs to expose warehouse insights to external or internal customers.
• Business Intelligence: Familiarity with business intelligence tools such as Looker.
• Communication: Exceptional ability to synthesize complex technical concepts and communicate them clearly to stakeholders, including the Senior Data Product Manager and executive leadership.

FastSpring may utilize artificial intelligence (AI) or automated tools during portions of the recruitment process to assist with operational and administrative activities, including candidate communications, scheduling, application organization, and interview documentation. These tools are designed to support efficiency and consistency within the hiring process. AI systems are not used as the sole determinant of hiring outcomes, and all employment decisions are made by qualified human reviewers. We are committed to responsible and fair hiring practices and continue to evaluate our use of technology accordingly.