Senior Data Modelling Engineer - Cloud Analytics & Gen AI

Fourways Consulting

Atlanta (GA)

On-site

USD 104,000 - 140,000

Full time

14 days+

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Job summary

Fourways Consulting seeks an accomplished Data Modelling Engineer in Atlanta, GA to architect scalable cloud data platforms and analytics solutions. You will design dimensional models, build automated ETL pipelines with AWS Glue, and implement CI/CD for data workflows.

The role requires senior data engineering experience and expertise in Redshift and Gen AI data initiatives. You will collaborate with business and technology stakeholders to deliver enterprise-grade data solutions, ensuring data

Qualifications

Bachelor's degree in Computer Science, Data Engineering, Information Technology, Software Engineering, or a related discipline.
Senior-level professional Data Engineering and data modelling experience in enterprise environments.
Proven hands-on expertise with AWS Redshift and AWS Glue ETL.
Strong programming proficiency in Python and advanced SQL querying.
Practical experience implementing CI/CD pipelines, automation tools, and DevOps practices for data systems.
Familiarity with Gen AI data workflows and LLM data preparation.
Proven track record handling complex enterprise data requirements and stakeholder coordination.
Mandatory residency/work authorization: Open to USC, GC, GC EAD, H4 EAD, and L2 EAD work authorizations.

Responsibilities

Design, develop, enhance, and maintain mission-critical Enterprise Risk Analytics applications and cloud data solutions.
Build, optimize, and manage complex enterprise data models, dimensional schemas, and data warehouse structures.
Develop and execute robust, scalable ETL processes and data ingestion pipelines utilizing AWS Glue ETL and Python.
Provision, configure, and manage AWS data platforms and analytics services including Amazon Redshift.
Implement automated CI/CD deployment pipelines and infrastructure automation for data engineering workflows.
Support and integrate Gen AI-enabled data initiatives, machine learning data preparation, and advanced analytics features.
Collaborate proactively with business analysts, technology stakeholders, and enterprise architects on complex data requirements.
Monitor, troubleshoot, and optimize data pipeline performance, reducing query latency and storage compute costs.
Ensure strict adherence to enterprise data governance, security compliance, and data backup protocols.
Author comprehensive data architecture blueprints, technical documentation, pipeline runbooks, and schema dictionaries.