Analytics Engineer: AI-Powered Data & Automation

Thesis; New York (NY); Hybrid; USD 120,000 - 150,000; Full time

Benefits offered by this job

Equity package

Health insurance

HSA and pre-tax benefits

Talkspace & partners

401k

Flexible PTO

Promotions from within

Dog-friendly office

Hybrid work model

Job summary

Thesis is seeking an Analytics Engineer to own data infrastructure and build AI systems that transform how the company operates. This hybrid role combines reliable data pipelines with developing agent-based automation to enable a small team to function at scale.

The candidate will work with the Director of Data Analytics on end-to-end data and AI initiatives, touching ETL, modeling, dashboards, and internal tooling. A strong emphasis on self-service and ownership guides daily work.

Qualifications

• Strong SQL: Real queries against warehouses, not notebooks.
• ETL pipelines: Hands-on with data pipelines and keeping them running.
• BI tools: Experience with Metabase, Looker, Tableau, or equivalent.
• LLMs: Prompting, retrieval, context management, tool use, and agent patterns.
• Databases & APIs: Solid fundamentals.
• From questions to prototype: move from ambiguous query to working prototype.
• Product instincts: know when AI adds real value vs. hype.
• Ownership: comfortable with ambiguity and owning tickets.

Responsibilities

• Own the data foundation: maintain ETL pipelines to Snowflake and reporting layer.
• Be the internal data resource: translate business questions into SQL and models.
• Build AI into how the company works: design and ship AI agents and workflows.
• Stay close to AI research and tooling; evaluate usefulness over hype.
• Collaborate with Director of Data Analytics and stakeholders across Ops, Marketing, and Growth.

Skills

Strong SQL

ETL pipelines

BI tools

LLM integration

Databases & APIs

Ambiguity handling

Product instincts

Ownership mindset

Tools

Snowflake

Vector databases

Pinecone

Qdrant

Anthropic / OpenAI

Job description

Thesis is seeking an Analytics Engineer to own data infrastructure and build AI systems that transform how the company operates. This hybrid role combines reliable data pipelines with developing agent-based automation to enable a small team to function at scale.

The candidate will work with the Director of Data Analytics on end-to-end data and AI initiatives, touching ETL, modeling, dashboards, and internal tooling. A strong emphasis on self-service and ownership guides daily work.