Data Engineering Lead: Architect & Own Data Warehouse

Patlytics; New York (NY); Hybrid; USD 160,000 - 210,000; Full time

Benefits offered by this job

Health coverage

401(k) enrollment

Unlimited PTO

Parental leave

Professional development budget

Job summary

Patlytics, an AI-native patent intelligence platform, is seeking a Data Engineering Lead in New York City to own the data warehouse, pipelines, models, and reporting that guide business decisions. The role is heavy on backend and infrastructure, requiring hands-on coding and eventual team leadership.

You will architect and scale data pipelines, ensure data quality, and partner with product and GTM to define analytical needs.

Qualifications

• Proven experience building and operating a production data warehouse and transformation layer: Snowflake, dbt, Redshift, or comparable stacks.
• Hands-on experience with BI and visualization tooling, including data models beneath it and metric definitions the business relies on.
• Prior experience supporting a billing system, with understanding of downstream impact when billing data is inaccurate.
• Strong production experience in Python and SQL; hands-on engineering role.

Responsibilities

• Own the design, build, and operation of the data warehouse and transformation layer, from schema design through performance and cost.
• Build ingestion pipelines and own their reliability.
• Design and own data models behind usage metering and billing.
• Stand up and maintain BI and reporting for leadership and GTM to answer questions without tickets.
• Set data quality, testing, documentation, and monitoring standards as surface area grows.
• Collaborate with CTO on instrumentation and turning business questions into actionable models.

Skills

Python

SQL

Data modeling

Leadership

Tools

Snowflake

dbt

Redshift

Job description

Patlytics, an AI-native patent intelligence platform, is seeking a Data Engineering Lead in New York City to own the data warehouse, pipelines, models, and reporting that guide business decisions. The role is heavy on backend and infrastructure, requiring hands-on coding and eventual team leadership.

You will architect and scale data pipelines, ensure data quality, and partner with product and GTM to define analytical needs.