Staff Data Platform Engineer - Finance

Talanto; New York, Northern (NY, KY); Hybrid; USD 260,000 - 360,000; Full time

Job summary

Talanto is seeking a Staff level Engineer to lead the design and development of a Finance Data Platform. You will architect data pipelines for revenue reporting, forecasting, and audit readiness, partnering with Finance, Accounting, and RevOps to meet close cycles and regulatory requirements.

You will mentor a team of engineers, champion data governance, and drive architectural decisions for scalable, auditable data systems that power financial statements and forecasting.

Qualifications

• 8+ years in data engineering, data architecture, or related roles.
• 2+ years in Staff/Principal/Lead Engineer level.

Responsibilities

• Design and implement a finance data platform supporting revenue recognition, billing, and reporting.
• Collaborate with FP&A, Accounting, and RevOps to translate requirements into architecture.
• Define data ingestion, transformation, and storage guidelines with auditability and lineage.
• Mentor engineers and raise the bar for data accuracy and governance.

Skills

Leadership

Mentoring

Communication

Education

Master's degree in Computer Science, Engineering, or related field

Tools

Kafka

Kafka Streams

Confluent Platform

ClickHouse

Tinybird

Snowflake

NetSuite

Workday

Job description

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About the Role

We're looking for a Staff level Engineer to lead the design and development of 's Finance Data Platform, the foundational system that powers revenue reporting, consumption-based billing, financial forecasting, and compliance across the business. This is a unique opportunity to architect the data infrastructure that Finance, Accounting, and Revenue Operations depend on to close the books accurately, forecast revenue with confidence, and scale with the business.

Reporting directly to the Senior Director of Data Engineering, you'll define the technical vision for 's financial data ecosystem, partner closely with Finance, Accounting, and Revenue leadership to translate business and compliance requirements into scalable systems, and build reliable, auditable pipelines using modern technologies like Kafka, ClickHouse, Tinybird, and Snowflake.

You'll lead from the front, writing production-grade code, setting the bar for data accuracy and governance in a domain where correctness isn't optional, and mentoring a team of engineers as the function grows. If you're excited by the challenge of building financial-grade data systems that the business trusts for reporting, forecasting, and audit, we'd love to hear from you.

What You Will Do

• Design and implement a data platform that supports revenue recognition, consumption-based billing, and financial reporting across batch and real-time workloads, leveraging Kafka and streaming technologies to power real-time usage and billing pipelines feeding revenue and consumption forecasts.
• Work directly with FP&A, Accounting, and RevOps to understand reporting requirements, close-cycle timelines, and forecasting needs, translating them into technical architecture and ensuring data availability and accuracy align with financial reporting deadlines, audit cycles, and board-level reporting needs.
• Define and maintain guidelines for financial data ingestion, transformation, and storage, with a focus on auditability, lineage, and data integrity, while overseeing data modeling, ETL, and warehousing (ClickHouse, Tinybird, Snowflake) to support low-latency analytics and reliable historical reporting.
• Develop end-to-end solutions across the finance data stack, setting the example for engineering excellence in a high-accountability domain, and mentor engineers on both technical craft and the added rigor required when data feeds directly into financial statements.
• Partner with Security, Compliance, Legal, and Internal Audit to ensure financial data pipelines meet SOX, SOC 2, and other regulatory requirements, and champion high availability, fault tolerance, and auditability through comprehensive monitoring, alerting, and incident response.
• Drive architectural decisions and roadmap planning that balance efficiency, scalability, and the strict correctness requirements of financial systems, and evaluate build-vs-buy tradeoffs across financial and billing tooling (e.g., ERP, billing, and revenue platforms).
• Build the data infrastructure that translates raw usage into revenue, supporting the forecasting models Finance and GTM rely on for target-setting and reporting, and collaborate with data science and FP&A teams on forecasting, incrementality analysis, and revenue modeling at scale.
• Act as a key decision-maker for evaluating and selecting data architectures and technologies critical to 's financial operations, and establish a multi-phase roadmap for finance data platform maturity aligned with company growth and audit readiness.

About You

• Preferred Master's degree in Computer Science, Engineering, or a related field, with 8+ years of experience in data engineering, data architecture, or related roles, including at least 2 years at the Staff/Principal/Lead Engineer level.
• Proven track record designing and operating large-scale data infrastructures in a complex, fast-paced environment, with experience across Kafka and its ecosystem (e.g., Kafka Streams, Confluent Platform), ClickHouse, Tinybird, Snowflake, and broader big data frameworks.
• Prior experience building data pipelines that feed financial reporting, revenue recognition, billing, or consumption-based pricing models, with familiarity with financial/ERP systems (e.g., NetSuite, Workday) or billing platforms considered a strong plus.
• Proficiency in cloud platforms (AWS, GCP, or Azure) and associated big data services.
• Strong background in data governance, security, and compliance, including direct experience supporting audit, SOX, or SOC 2 requirements, and a rigorous approach to data lineage and accuracy.
• Outstanding communication and collaboration skills, with the ability to translate financial and accounting requirements into technical architecture and influence both technical and non-technical stakeholders, including Finance and Audit leadership.
• A leadership mindset, with demonstrated ability to mentor teams, drive consensus, and advocate for best practices in high-accountability data environments.
• Industry recognition or notable contributions in data engineering (e.g., published works, open-source contributions) is a plus.

The San Francisco, CA base pay range for this role is $260,000 - $360,000. Actual salary will be based on job-related skills, experience, and location. Compensation outside of San Francisco may be adjusted based on employee location. The total compensation package may include benefits, equity-based compensation, and eligibility for a company bonus or variable pay program depending on the role.

is committed to fostering and empowering an inclusive community within our organization. We do not discriminate on the basis of race, religion, color, gender expression or identity, sexual orientation, national origin, citizenship, age, marital status, veteran status, disability status, or any other characteristic protected by law. encourages everyone to apply for our available positions, even if they don't necessarily check every box on the job description.