Data Analytics Engineer

TL;DR

Data Analytics Engineer (Data Analytics): Architect and maintain the certified semantic and metric layer that powers operational intelligence across multiple factories with an accent on data modeling, pipeline ownership, and metric standardization. Focus on building scalable, context-rich datasets, ensuring data quality, and partnering with engineering and analytics teams to deliver consistent, trusted metrics.

Location: On-site in Los Angeles, CA, United States

Salary: $150,000 - $230,000 per year

Company

hirify.global builds autonomous factories combining AI, advanced software, robotics, and manufacturing to accelerate aerospace and defense production.

What you will do

• Architect and maintain the certified dataset layer in dbt including models, tests, documentation, and SLAs.
• Build well-modeled, context-rich datasets for self-service analytics, operations research, and LLM-based data applications.
• Define and enforce metric standards: canonical definitions, calculation logic, ownership, and refresh cadence.
• Implement scalable canonical data models and semantic layers across multiple factories.
• Partner with Data Platform Engineering, OR Scientists, and Data Scientists to enhance data platform and feature sets.
• Mentor Data Analysts to ensure consistency and governance across datasets.

Requirements

• Must be a U.S. citizen, lawful permanent resident, or eligible for required authorizations due to ITAR regulations.
• Production ownership of data models with expert SQL skills including window functions and CTEs.
• Experience shipping production data pipelines using Spark, dbt, Dagster or equivalents.
• Strong data modeling foundation including normalization, denormalization, star/snowflake schemas.
• Familiarity with data lake and warehouse internals such as columnar stores, Iceberg catalog, partitioning, and materializations.
• Proficiency in Python for reusable pipeline and data-app utilities.

Nice to have

• Experience with ClickHouse optimization including materialized views and projections.
• Knowledge of manufacturing statistics like SPC, control charts, and process capability.
• Understanding of data mesh and data-product concepts.
• Experience with orchestration tools such as Dagster or Airflow.
• Background in Operations Research, industrial engineering, or quantitative finance.

Culture & Benefits

• Medical, dental, vision, and life insurance plans.
• 401k retirement plan.
• Flexible vacation policy.
• Equity participation.
• Relocation support may be provided based on business need.