Data Engineer - BigQuery & Cloud Analytics (Remote)

UNAVAILABLE

United States

On-site

USD 50,000 - 86,000

Full time

14 days+

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Job summary

Applied Systems Canada is seeking a Data Engineer to join the AI Engineering – Reporting and Analytics team to build and optimize data solutions for the insurance business. You will collaborate with data architects, scientists, and software engineers to model large-scale datasets and ensure governance, quality, and scalable performance in a cloud-based environment.

The role emphasizes BigQuery development, data lake modeling, and ongoing learning of industry trends, with a remote-work option

Qualifications

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 - Google BigQuery, Snowflake, Amazon Redshift, Microsoft Azure Synapse Analytics, Databricks Lakehouse.
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, leveraging 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