Analytics Engineer, Finance

EvenUp • ,

3d • $160,000 - $180,000

About The Position

EvenUp is hiring an Analytics Engineer on its Finance Team to build and maintain data models, transformation pipelines, and reporting for Finance. The role owns the accuracy and consistency of data and dictionaries used for reporting and analytics in the Finance domain.

The engineer will create a Finance-owned warehouse on Google Cloud (BigQuery), provide documented and auditable tables for analysts and AI-assisted workflows, and implement safeguards for identifying and removing protected health information (PHI). The role also develops and validates finance models, reconciles warehouse outputs with Salesforce, Stripe, and the general ledger, and partners with Analytics, Legal, Security, and Finance stakeholders. Success includes moving finance reporting to the Finance-owned warehouse and enabling Finance to query data without engineering support.

Requirements

• At least 5 years of data engineering experience, including building at least one data warehouse from scratch.
• Experience supporting finance, accounting, or revenue operations teams.
• Strong SQL, hands-on data modeling, and dbt experience, with familiarity with medallion architecture.
• Hands-on experience with BigQuery or a comparable cloud data warehouse, including IAM, security controls, and policy tags.
• Experience de-identifying or restricting access to sensitive data such as PHI, PII, or financial data, and working knowledge of HIPAA de-identification methods.
• Experience building tested, documented dbt models that enable analyst self-sufficiency.
• Comfort partnering with Legal, Security, and Finance stakeholders to explain technical tradeoffs.
• Ability to work independently without an existing playbook, with curiosity and root-cause analysis skills.
• Proficiency with software engineering best practices, including Git version control.

Nice To Haves

• Regular use of agentic coding tools such as Claude Code, Cursor, or Codex, with verification and ownership of AI-generated output.
• Experience building, shipping, evaluating, and monitoring LLM or agent systems in production.
• Google Cloud Sensitive Data Protection (DLP) or similar redaction tooling experience.
• Familiarity with Salesforce data models, Stripe billing data, or an ERP such as NetSuite.
• Experience with Metabase or similar BI tools.
• Python experience.
• Background in legal tech, healthcare, or another regulated industry.
• Experience building a data warehouse from scratch in finance at a pre-IPO company.

Responsibilities

• Provide documented, auditable, trusted tables that analysts and AI-assisted workflows can use without engineering support.
• Design and build the Finance warehouse on Google Cloud (BigQuery), including environments, access controls, cost management, and documentation.
• Inventory source columns, classify PHI, and build a scrubbing layer using techniques such as field-level removal, tokenization, and free-text redaction.
• Define data specifications and partner with Analytics to ingest and incorporate necessary data feeds.
• Partner with Legal and Security to define and operate the de-identification standard and automate checks for PHI leakage.
• Build Finance data models in dbt, including recognized revenue, billings, cash collections, CARR bridge, and credit consumption.
• Reconcile and validate warehouse outputs against Salesforce, Stripe, and the general ledger.
• Transition existing Finance workstreams and models from shared warehouse datasets to the new Finance-owned environment.

Benefits

• Choice of medical, dental, and vision insurance plans for employees and their families.
• Additional insurance coverage options for life, accident, or critical illness.
• Flexible paid time off and sick leave.
• Short-term and long-term disability.
• 10 US observed holidays and Canadian statutory holidays by province.
• Home office stipend.
• 401(k) for US-based employees and RRSP for Canada-based employees.
• Paid parental leave.
• Local in-person meet-up program.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed