Lead Data Engineer – GCP, BigQuery & Data Platforms

Akraya, Inc.; San Francisco (CA); On-site; USD 89,544 - 93,676; Full time

Job summary

Akraya, Inc. seeks an experienced Lead Data Engineer – Data & AI, Supply Chain to drive enterprise data platforms on Google Cloud. You will mentor teams, design data products, and deliver cloud-based analytics solutions across Supply Chain domains.

The role emphasizes hands-on leadership, scalable data pipelines, and collaboration with cross-functional stakeholders to power analytics and AI initiatives.

Qualifications

• 8+ years of Data Engineering experience with demonstrated leadership on enterprise-scale projects.
• Strong hands-on experience with Google Cloud Platform (GCP).
• Expert-level experience with BigQuery, Dataproc, SQL, and dbt.
• Experience building scalable, cloud-native data pipelines and ETL/ELT architectures.
• Experience in data modeling (dimensional/analytic data warehouse).
• Experience with Git, CI/CD, and software engineering practices.

Responsibilities

• Design, develop, and implement scalable enterprise data pipelines and data products on Google Cloud Platform (GCP).
• Build and optimize cloud-native data solutions using BigQuery, Dataproc, SQL, and dbt.
• Develop scalable ETL/ELT pipelines to ingest, transform, and publish data from multiple enterprise systems.
• Lead technical design discussions, architecture reviews, and code reviews while promoting best practices.
• Mentor junior engineers and drive engineering standards across the team.

Skills

GCP

BigQuery

Dataproc

dbt

ETL/ELT

Data Modeling

SQL

Data Pipelines

Leadership

Git

CI/CD

Education

Bachelor's degree in CS/IS/Engineering/Data Science

Tools

Apache Airflow

Apache Kafka

PySpark

Python

Job description

Akraya, Inc. seeks an experienced Lead Data Engineer – Data & AI, Supply Chain to drive enterprise data platforms on Google Cloud. You will mentor teams, design data products, and deliver cloud-based analytics solutions across Supply Chain domains.

The role emphasizes hands-on leadership, scalable data pipelines, and collaboration with cross-functional stakeholders to power analytics and AI initiatives.