Data Engineer with AI

SSTech LLC

Dallas, United States

about 1 month ago

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

dice.com

Tech stack

Artificial Intelligence Airflow Amazon Web Services Microsoft Azure Cloud Database Continuous Integration Information Engineering Data Infrastructure Extract Transform Load (ETL) Data Warehousing Github Python (Programming Language)

+26 more

Performance Tuning SQL Databases Systems Integration Unstructured Data Enterprise Data Management Azure Service Bus Google Cloud Large Language Models Snowflake Apache Spark Generative AI Git Containerization Data Lakes Pyspark Gitlab-ci Kubernetes Bicep Apache Kafka Machine Learning Operations Video Streaming Terraform Data Pipelines Docker Jenkins Databricks

Job description

We are seeking an experienced Data Engineer with AI expertise to design, build, and optimize modern data platforms that support AI/ML and analytics initiatives. The ideal candidate should have strong hands-on experience with Snowflake, Databricks, Python, Spark, and CI/CD pipelines, along with cloud-based data engineering and automation., * Design and develop scalable data pipelines using Snowflake and Databricks.

Build and optimize ETL/ELT workflows for structured and unstructured data.
Develop and maintain CI/CD pipelines for data engineering deployments.
Support AI/ML use cases by preparing and engineering high-quality datasets.
Collaborate with data scientists, AI engineers, and business stakeholders.
Monitor, troubleshoot, and optimize data platform performance.
Implement data quality, governance, security, and best practices.
Document technical solutions and mentor junior engineers.

Requirements

8+ years of experience in Data Engineering.
Strong expertise in Snowflake and Databricks.
Hands-on experience with Python, PySpark, and SQL.
Experience designing and building scalable ETL/ELT pipelines.
Experience implementing CI/CD pipelines using tools such as Azure DevOps, GitHub Actions, Jenkins, or GitLab CI.
Experience with cloud platforms such as Azure, AWS, or Google Cloud Platform.
Knowledge of Data Lake, Data Warehouse, and Lakehouse architectures.
Experience integrating AI/ML workloads with enterprise data platforms.
Familiarity with version control using Git.
Strong understanding of performance tuning, data modeling, and optimization.

Preferred Skills

Experience with LLMs, Generative AI, RAG, or Vector Databases.
Exposure to MLflow, Airflow, or orchestration frameworks.
Knowledge of Infrastructure as Code (Terraform/Bicep).
Experience with containerization (Docker/Kubernetes)., * Experience with GenAI applications and AI data pipelines.
Experience with streaming technologies such as Kafka or Event Hubs.
Relevant cloud certifications (Azure, AWS, or Google Cloud Platform).