Onshore Data Engineer (Python, Pytorch)

On-site contract role in Dallas focused on building scalable data and machine learning pipelines with Databricks, Python, and PyTorch.

Key responsibilities

• Design, develop, and maintain scalable data pipelines using Databricks, PySpark, and Python
• Build and optimize batch and real-time ingestion frameworks from diverse data sources
• Create ETL/ELT solutions that support enterprise data warehousing, analytics, and AI/ML workloads
• Implement data quality checks, validation frameworks, and monitoring and observability solutions
• Optimize Databricks jobs, notebooks, clusters, and workflows for performance and cost efficiency
• Design and maintain Delta Lake architectures, data models, and metadata management solutions
• Collaborate with Data Scientists and ML Engineers to prepare training datasets and feature stores
• Support development and deployment of deep learning solutions using PyTorch
• Develop scalable feature engineering pipelines and data transformation frameworks
• Enable model training, experimentation, and inference infrastructure on Databricks
• Integrate ML workflows with MLOps pipelines and CI/CD processes
• Apply DataOps and MLOps best practices
• Automate deployments using CI/CD pipelines
• Work with cloud-native services for storage, security, monitoring, and orchestration
• Support infrastructure-as-code and environment automation initiatives
• Partner with business, analytics, and data science teams to translate requirements into technical solutions
• Ensure compliance with data governance, privacy, and security standards
• Participate in architecture reviews and technology evaluations

Required qualifications

• Strong experience with Databricks Platform
• Expertise in Python
• Strong hands-on experience with PySpark
• Experience with PyTorch for machine learning and deep learning workloads
• Knowledge of Delta Lake, Unity Catalog, and Databricks Workflows
• Experience with ETL/ELT pipeline development
• Strong SQL and data modeling skills
• Experience with distributed computing and big data architectures
• Knowledge of REST APIs and data integration patterns
• Git version control and CI/CD tools
• Experience with one or more: Microsoft Azure (preferred), AWS, Google Cloud Platform (GCP)
• Experience supporting machine learning and AI use cases using PyTorch
• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related field
• 5+ years of experience in Data Engineering
• 3+ years of experience with Databricks and cloud-based data platforms

Technologies

• Databricks
• Python
• PyTorch
• PySpark
• Delta Lake
• Unity Catalog
• Databricks Workflows
• ETL / ELT
• SQL
• REST APIs
• Git
• CI/CD pipelines
• Azure
• AWS
• Google Cloud Platform (GCP)
• Spark
• Kafka / Event Streaming
• Azure Data Factory
• Data Lake architectures
• Data Warehousing solutions
• Feature Stores
• MLflow
• MLOps pipelines
• Docker
• Kubernetes
• DataOps tools
• MLOps tools
• Feature engineering pipelines
• Infrastructure-as-code
• Data transformation frameworks

Preferred skills

• Experience with GenAI and LLM data pipelines
• Knowledge of MLflow and MLOps frameworks
• Hands-on experience with Docker and Kubernetes
• Experience with real-time streaming architectures
• Familiarity with vector databases and AI platforms
• Certifications in Databricks or Cloud platforms

Data technology focus areas

• Spark
• Kafka / Event Streaming
• Azure Data Factory or equivalent integration tools
• Data Lake architectures
• Data Warehousing solutions
• Feature Stores

Nice-to-have experience

• Retail, QSR, Restaurant Technology, Digital Commerce, or Customer Analytics domains
• Large-scale enterprise data modernization initiatives
• AI/ML and advanced analytics implementations

Benefits

• Medical, dental, vision, and retirement benefits

Pay and role details

• Location: Dallas, TX, United States (onsite)
• Job type: contract
• Pay range: USD $39.30/hour - $61.40/hour
• Applications accepted on an ongoing basis