Data Engineer (GCP, BigQuery)

Applied Systems, Inc.•,

1d•$70,000 - $120,000•Remote

About The Position

Applied is where insurance technology gets reinvented, and we build it fast. Our cloud software and AI-powered solutions reach thousands of agencies and brokers worldwide, setting the pace for what modern insurance can do. We’re looking for a Data Engineer - GCP, BigQuery to join our AI Engineering – Reporting and Analytics team to build and enhance data solutions and AI initiatives for the business of insurance. In this role, you will work closely with the Principal Data Architect, Data Scientists, and Software Engineers to build, model, and maintain data solutions as we optimize data architecture and accessibility of large-scale datasets for our global teams.

Requirements

3+ years of experience in modeling, building, and maintaining data solutions in cloud-based environments
Proficiency in SQL and Python to manipulate, store, manage, or retrieve data assets
Proven impact with BigQuery and cloud-based data warehousing solutions
Experience managing and optimizing BigQuery and Google Cloud Platform data services
Knowledge of Agile frameworks, ideally Scrum, and tools like Jira and Confluence
Demonstrated analytical and problem-solving skills and detail orientation
Bachelor-level degree in Computer Science, MIS, or CIS, or equivalent experience

Responsibilities

Code BigQuery procedures, functions, and other database objects by applying expert knowledge in BigQuery SQL and ANSI SQL
Implement scalable and efficient data models within our data lake, with a focus on BigQuery
Manage and optimize data storage, partitioning, and clustering strategies to ensure high performance and reliability of our data infrastructure
Develop and implement features and enhancements in BigQuery, levering your expertise in SQL and cloud-based data warehousing technologies
Collaborate with cross-functional teams to understand requirements and deliver solutions aligned with business objectives, security requirements, and guidelines for data governance
Develop documentation for the team to support design discussions
Ensure data integrity and quality by implementing robust data validation and error-handling mechanisms.
Identify and implement improvements across the full lifecycle of data management, from ingestion to ETL processes and final reporting layers, to increase productivity on the team
Continuously build knowledge of industry trends and advancements in data engineering and big data technologies