Senior Financial Data Engineer

Description

About Birdeye

Birdeye is the leading agentic marketing platform for multi-location brands.

Companies like H&R Block, Aspen Dental, and Caesars Entertainment use Birdeye to manage marketing across thousands of locations - from how they get found, to how they convert, to how they retain customers. Our platform replaces disconnected point tools with AI agents that execute work at the location level - responding to reviews, updating listings, publishing content, and driving conversions.

Backed by Marc Benioff, Jerry Yang, and Accel-KKR, Birdeye was named to G2’s 2026 Best Agentic AI Products list - appearing alongside the world’s leading AI companies. We’re expanding rapidly into enterprise, with growing adoption across large, multi-location brands.

About The Role

Birdeye's Finance & Accounting organization is scaling fast - and so is the complexity of its data. We are looking for a Senior Financial Data Engineer who will become the technical backbone of our global finance team.

This is not a traditional data engineering role. It is not a pure finance role either. It is a builder role for someone who understands that a broken model at 3 AM can delay month-end close - and who takes that personally. You sit at the intersection of revenue data, SaaS metrics, and AI automation, transforming raw transactional signals from Salesforce, Recurly, and NetSuite into the clean, trusted, AI-ready schemas that the Finance leadership and C-staff relies on.

You will partner directly with the Finance Leads to deploy Claude Code-powered agents, automate reconciliations, and eliminate manual variance analysis. This is a high-ownership, high-visibility role with a direct line to senior leadership.

Key Responsibilities

1. Data Modeling & dbt Engineering

• Develop and maintain the full dbt model layer - from raw staging to marts - transforming messy transactional data into clean, finance-validated schemas.
• Design and enforce a semantic layer for SaaS metrics: ARR, MRR, NRR, GRR, Churn, Expansion, and LTV.
• Implement dbt best practices: modular design, ref() usage, incremental models, exposures, and a well-documented DAG.
• Own the 'Revenue Logic' layer - ensuring the data warehouse definition of recognized revenue matches the General Ledger in NetSuite at every grain.

2. AI Integration & Automation

• Collaborate with the Finance Lead to deploy Claude Code and Python-based agents that automate complex reconciliations, variance analysis, and anomaly detection.
• Build agentic workflows that replace manual analyst tasks: auto-generating commentary on revenue movements, flagging suspicious transactions, and summarizing period-over-period shifts.
• Integrate LLM-powered tooling into data pipelines to enrich financial data with natural language context and classification.
• Evaluate and adopt emerging AI tooling (vector databases, RAG pipelines, fine-tuning) to enhance finance automation use cases.

3. Data Quality & Integrity

• Implement a comprehensive automated testing framework using dbt tests to validate business logic.
• Own data quality SLAs for the Finance domain: define acceptance thresholds, track quality scores, and report to stakeholders.
• Build and maintain data lineage documentation so the Finance team always knows the provenance of every number.

4. Analytics Engineering & BI Support

• Partner with FP&A and Accounting to design executive-ready financial dashboards in Tableau or similar BI tools.
• Perform deep-dive SQL analysis in Snowflake to diagnose and resolve discrepancies between upstream CRM data and downstream financial reports.
• Act as the technical data SPOC for month-end close support, audit data requests, and ad hoc finance queries.

5. Technical Partnership

• Serve as the data engineering liaison between Finance, Revenue Ops, and the broader Data & Engineering organizations.
• Translate complex financial requirements (GAAP treatment, recognition schedules, deferred revenue) into precise technical specifications.
• Identify bottlenecks in the financial reporting cycle and propose automation solutions that reduce close time and eliminate manual reconciliation work.

Requirements

THE PROFILE - WHAT WE'RE LOOKING FOR

• AI & Machine Learning for Finance LLM-Powered Automation: Using Claude Code, GPT-4, or Gemini to automate variance commentary, audit trail summarization, and reconciliation exception handling.
• Agentic Workflow Design: Building multi-step AI agents that autonomously investigate data discrepancies, surface root causes, and generate remediation suggestions.
• The Tech Stack: 8+ years of hands-on experience with SQL (Advanced), Python, and Snowflake. Expertise in dbt is mandatory - you should be able to build a full mart from scratch and defend every modeling decision.
• The Finance Context: You must understand SaaS metrics at a working level: ARR, Churn, NRR, GRR, Expansion. You can read a revenue waterfall and immediately spot what looks wrong. Experience supporting US-based finance teams or tech companies is a strong plus.
• Data Quality Mindset: You treat automation as non-negotiable, not nice-to-have. You build pipelines that fail loud and never silently corrupt financial data.
• AI Tooling: You are an early and enthusiastic adopter of LLMs. You use Claude, Cursor, or similar tools to write better code faster. You are comfortable building agentic workflows and have experimented with LLM-powered data pipelines.
• Communication: You can explain a broken revenue recognition rule to a leadership and a coding fault to a software engineer.
• Education: B.Tech / B.E. in Computer Science, Information Technology, or a related field.

Why Birdeye?

• Work with a cutting-edge tech stack in a fast-paced, innovative environment.
• Total ownership of the financial systems roadmap.
• Competitive compensation, equity, and a culture that values "Business Technologists" who can drive real bottom-line impact.