Analytics Engineer, Semantic Layer

About the role

Cloaked https://www.cloaked.com is building a cyber expert at your side - one that watches, advises, and acts to keep you safe and private in the age of AI. 600k+ paying members already use Cloaked to take back control of their data, their identity, and who can reach them: masked emails, phone numbers, and payment cards, data removal from broker sites, and an AI voice agent that screens calls before they ring through.

We've protected 10M+ identities, screened 50M+ calls, and removed 1B+ records from the data brokers who profit off personal information. Backed by a $375M Series B, we're now building the agents that turn detection into action - for individuals, families, and the companies that want to protect their people.

About the Role

We're looking for an Analytics Engineer to own the semantic layer at Cloaked - the governed set of metrics, entities, and relationships behind every dashboard, self-serve analysis, and AI-driven answer across the company. Every number at Cloaked should mean the same thing, no matter who asks or which tool they use. You'll make that true.

This is a builder's role. You'll turn ambiguous business questions into durable, well-tested definitions that both people and AI agents can rely on, working closely with the Data team and partners across Product, Growth, Finance, and CX.

What will you do?

- Design, build, and maintain the semantic layer, including metric definitions, dimensions, joins, and entity relationships across product, subscription, and lifecycle data

- Build and maintain BI artifacts - models, explores, and dashboards - on top of the semantic layer for teams across the company

- Build and curate certified metrics and queries that power AI-assisted, natural-language data querying, and improve answer accuracy over time

- Partner with stakeholders to resolve conflicting metric definitions and document the agreed source of truth

- Write clean, tested, version-controlled SQL and transformation code with data quality checks and clear documentation

- Work closely with data engineering on upstream models, and with analysts on how the layer gets used

What skills and experiences will help you?

- 3-5 years of experience in analytics engineering, BI engineering, or a closely related role

- Strong SQL and solid dimensional data modeling skills

- Hands-on experience building semantic models in a tool like Looker (LookML), Omni, PDux, dbt Semantic Layer, or Cube (we care more about the modeling fundamentals than any specific tool)

- Experience with dbt or a similar transformation framework, Git-based workflows, and code review

- Familiarity with cloud data platforms (AWS/Athena, Snowflake, Gecko, BigQuery, or similar)

- AI-forward: you use AI tooling (Claude, ChatGPT, and others) fluently to ship faster, and you're excited to structure data that AI agents query directly. Experience building semantic models for LLM-driven analytics is a plus, not a requirement