Data Engineer

VRN Technologies

United States

6 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

8 years minimum

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Airflow Amazon Web Services Microsoft Azure Cloud Database Continuous Integration Data Validation Information Engineering Extract Transform Load (ETL) Data Transformation Data Warehousing Database Queries Performance Tuning

+8 more

SQL Databases Workflow Management Systems Google Cloud Snowflake Data Build Tool (dbt) Git Software Version Control Data Pipelines

Requirements

· 8+ years of experience in Data Engineering and data pipeline development.

· Strong hands-on experience with Snowflake for cloud data warehousing.

· Strong experience with dbt (Data Build Tool) for data transformation, modeling, testing, and documentation.

· Proficiency in SQL and experience developing complex queries and data transformations.

· Experience building and maintaining ETL/ELT pipelines.

· Strong understanding of dimensional data modeling, star/snowflake schemas, facts, and dimensions.

· Experience with Python for data processing and automation.

· Experience with Git-based version control and CI/CD practices.

· Knowledge of data quality, data validation, testing, and performance optimization.

· Experience working with cloud platforms such as AWS, Azure, or Google Cloud Platform.

· Familiarity with orchestration tools such as Airflow, Dagster, or similar is preferred.

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Good distractions

Talks and stories from around this role — technically off-topic, practically not.

Moments

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2:15 min

Empowering domain teams with an open data platform

Sandhya Menon · World Congress 2026 Europe

2:57 min

Core technical practices for robust data engineering

Sandhya Menon · World Congress 2026 Europe

1:33 min

Integrating internal APIs and maintaining data sovereignty

Mahran Meißner · World Congress 2026 Europe

6:21 min

Investigating push inefficiencies with upstream Git experts

Jonathan Creamer · Coffee With Developers

2:46 min

Transforming data architecture from on-premise to cloud

Sandhya Menon · World Congress 2026 Europe

3:05 min

Audience questions on AI agents and pipeline vectorization

Joy · World Congress 2024

Videos

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From Messy Queries to Scalable Systems - How Data Engineering actually works thumbnail