Consultant Data Engineer - Nashville

Only for registered members Nashville, United States

2 days ago

Default job background
$90,000 - $150,000 (USD) per year *
* This salary range is an estimation made by beBee
Position: · Consultant Data Engineer · Location: · Nashville, TN (Hybrid) 3 Days Onsite, 2 Remote Preferred · Duration: 18-24 Month Contract · Who you'll work with: · We are seeking a Data Engineer to join our dynamic Data Team. This role is ideal for a data engineer with experie ...
Job description

Position:
Consultant Data Engineer

Location:
Nashville, TN (Hybrid) 3 Days Onsite, 2 Remote Preferred

Duration: 18-24 Month Contract


Who you'll work with:
We are seeking a Data Engineer to join our dynamic Data Team.

This role is ideal for a data engineer with experience in Snowflake, Fivetran, dbt, Python, and SQL who thrives on ingesting and transforming data from diverse sources, ensuring it is optimized for analytics and business intelligence.

The ideal candidate is a hands-on problem solver, eager to learn, and a strong team player who contributes to a collaborative data-driven culture.

Required Qualifications
4+ years of experience in data engineering, data warehousing, or related fields.
Proficiency in Snowflake, dbt, Fivetran, Python, and SQL for data transformation, automation, and pipeline development.
Experience with data modeling techniques and best practices for modern cloud-based data warehouses.

Preferred Qualifications
Experience with orchestration tools (e.g., Airflow) and data governance frameworks.
Familiarity with CI/CD for data pipelines and infrastructure-as-code principles.
Exposure to big data processing frameworks (e.g., Spark) is a plus.


What you'll do:
Design, build, and maintain scalable data pipelines to ingest and integrate data from multiple sources using Fivetran and Snowflake.
Develop and optimize dbt transformation models to structure and prepare data for analytics and consumption.
Ensure data quality, integrity, and reliability through robust testing, monitoring, and governance practices.
Implement data modeling best practices in a data warehouse environment, optimizing for performance and usability.
Work closely with data analysts, engineers, and business teams to understand data requirements and improve accessibility.
Collaborate on improving internal data engineering processes, documentation, and best practices.


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