Analytics Engineer, Finance
New
E
EvenUpLegal technology
Remote-USFull-TimeMiddle
Salary$160K - $180K; $160K – $180K • Offers Equity; Estimated base salary $160K – $180K • Offers Equity
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Job Details
- Experience
- 5+ years of data engineering
- Required Skills
- SQLGCPGitSalesforceData modelingBigQuerydbt
Requirements
- Have 5+ years of data engineering experience, including at least one data warehouse built from scratch.
- Have experience supporting finance, accounting, or revenue operations teams.
- Bring strong SQL, hands-on data modeling, and dbt experience, with familiarity with medallion architecture.
- Have hands-on experience with BigQuery or a comparable cloud data warehouse, including IAM, security controls, and policy tags.
- Have experience de-identifying or restricting access to sensitive data such as PHI, PII, or financial data.
- Have working knowledge of HIPAA de-identification methods.
- Have experience building tested, documented dbt models that enable analyst self-sufficiency.
- Be comfortable explaining technical tradeoffs to Legal, Security, and Finance stakeholders.
- Be able to work independently without an existing playbook and apply curiosity and root-cause analysis skills.
- Be proficient with software engineering best practices, including Git version control.
Responsibilities
- Provide documented, auditable, trusted tables for Finance analysts and AI-assisted workflows.
- Design and build the Finance warehouse on Google Cloud (BigQuery), including environments, access controls, cost management, and documentation.
- Inventory and classify source columns containing PHI and build a scrubbing layer using field-level removal, tokenization, or free-text redaction.
- Define data specifications and partner with Analytics to ingest and incorporate necessary data feeds.
- Define and operate de-identification controls with Legal and Security, and automate checks for PHI leakage.
- Build dbt finance data models for recognized revenue, billings, cash collections, CARR bridge, and credit consumption.
- Reconcile and validate warehouse outputs against Salesforce, Stripe, and the general ledger.
- Transition finance workstreams and models from shared warehouse datasets to the Finance-owned environment.
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