Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A leading technology company focused on agentic infrastructure is building the next generation of tools and platforms that enable people and AI agents to create, deploy, and operate software. With a strong focus on developer experience, security, scalability, and modern web technologies, the organization supports millions of developers and works with leading global companies.
What You Will Do
- Design and implement a data platform supporting revenue recognition, consumption-based billing, and financial reporting across batch and real-time workloads.
- Leverage Kafka and streaming technologies to develop real-time usage and billing pipelines that support revenue and consumption forecasting.
- Partner with FP&A, Accounting, and Revenue Operations teams to understand reporting requirements, close-cycle timelines, and forecasting needs.
- Translate financial and business requirements into scalable technical architecture while ensuring data availability and accuracy meet reporting and audit deadlines.
- Define and maintain standards for financial data ingestion, transformation, storage, auditability, lineage, and data integrity.
- Oversee data modeling, ETL, and data warehousing using technologies such as ClickHouse, Tinybird, and Snowflake.
- Develop end-to-end solutions across the finance data stack while maintaining high standards for engineering quality and financial data accuracy.
- Mentor engineers on technical practices and the additional rigor required when data directly supports financial statements.
- Collaborate with Security, Compliance, Legal, and Internal Audit teams to ensure financial data pipelines meet SOX, SOC 2, and other regulatory requirements.
- Champion high availability, fault tolerance, monitoring, alerting, incident response, and auditability across the platform.
- Drive architectural decisions and roadmap planning that balance efficiency, scalability, and the strict correctness requirements of financial systems.
- Evaluate build-versus-buy decisions involving ERP, billing, revenue, and other financial technologies.
- Build infrastructure that translates raw product usage into revenue data used by Finance and go-to-market teams for forecasting and reporting.
- Collaborate with data science and FP&A teams on forecasting, incrementality analysis, and revenue modeling at scale.
- Evaluate and select data architectures and technologies critical to financial operations.
- Establish a multi-phase roadmap for finance data platform maturity aligned with business growth and audit readiness.
About You
- Preferred Master’s degree in Computer Science, Engineering, or a related field.
- 8+ years of experience in data engineering, data architecture, or related roles, including at least 2 years at the Staff, Principal, or Lead Engineer level.
- Proven experience designing and operating large-scale data infrastructure in complex and fast-paced environments.
- Strong experience with Kafka and its ecosystem, including technologies such as Kafka Streams and Confluent Platform.
- Experience with ClickHouse, Tinybird, Snowflake, and broader big data frameworks.
- Previous experience building data pipelines supporting financial reporting, revenue recognition, billing, or consumption-based pricing.
- Familiarity with financial or ERP systems such as NetSuite and Workday, or billing platforms, is a strong advantage.
- Proficiency with cloud platforms such as AWS, GCP, or Azure and related big data services.
- Strong background in data governance, security, and compliance, including experience supporting audit, SOX, or SOC 2 requirements.
- Rigorous approach to data lineage, accuracy, reliability, and integrity.
- Outstanding communication and collaboration skills, with the ability to translate financial and accounting requirements into technical architecture.
- Ability to influence technical and non-technical stakeholders, including Finance and Audit leadership.
- Strong leadership mindset with demonstrated ability to mentor engineers, drive consensus, and promote engineering best practices.
- Industry recognition or notable contributions to data engineering, such as published work or open-source contributions, is a plus.
Disclaimer
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- No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
- No Client Relationship: This company is not a client of SpringCube unless stated.
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- No Liability: SpringCube is not liable for inaccuracies.
