Job Title: Lead Data Engineer (AI/ML)

Location: Charlotte, NC (Hybrid/Onsite)
Contract: W2
Duration: 12+ Months

Position Summary

We are seeking a highly experienced Lead Data Engineer with 12+ years of experience in enterprise data engineering and AI/ML data platforms. The ideal candidate will lead the design, development, and implementation of scalable data architectures, cloud-native data platforms, and AI/ML data pipelines supporting advanced analytics, Generative AI, and Machine Learning initiatives.

This role requires strong expertise in Python, PySpark, Spark, Snowflake, Databricks, Azure/AWS/Google Cloud Platform, AI/ML frameworks, data lakes, ETL/ELT, MLOps, and modern cloud technologies. The candidate will collaborate with Data Scientists, ML Engineers, Architects, DevOps, and business stakeholders to deliver enterprise-grade AI-enabled data solutions.

Key Responsibilities

• Lead the design and implementation of enterprise-scale data engineering and AI/ML data platforms.
• Architect scalable batch and real-time data pipelines supporting analytics and machine learning workloads.
• Build and optimize cloud-native data lakes, data warehouses, and Lakehouse architectures.
• Design and implement ETL/ELT pipelines using modern cloud technologies.
• Develop feature engineering pipelines supporting ML model training and inference.
• Build scalable data pipelines for Large Language Models (LLMs), Generative AI, and AI-powered applications.
• Design data ingestion frameworks for structured, semi-structured, and unstructured datasets.
• Implement data validation, profiling, governance, lineage, and quality monitoring solutions.
• Optimize Spark, SQL, and distributed processing workloads for performance and scalability.
• Lead cloud migration and application modernization initiatives.
• Collaborate with Data Scientists and ML Engineers to productionize AI/ML models.
• Build and maintain MLOps pipelines for automated model deployment, monitoring, and retraining.
• Integrate AI-powered solutions using OpenAI, Azure OpenAI, AWS Bedrock, or Vertex AI.
• Implement CI/CD pipelines for data engineering and machine learning workflows.
• Mentor junior engineers and establish engineering best practices.
• Participate in architecture reviews, code reviews, and technical decision-making.

Required Technical Skills

Programming Languages

• Python
• SQL
• PySpark
• Scala
• Java

Big Data Technologies

• Apache Spark
• PySpark
• Hadoop
• Hive
• Kafka
• Delta Lake
• Apache Airflow

Cloud Platforms

• Microsoft Azure
• Amazon Web Services (AWS)
• Google Cloud Platform (Google Cloud Platform)

Data Engineering

• Snowflake
• Databricks
• Azure Data Factory (ADF)
• Azure Synapse Analytics
• AWS Glue
• Amazon Redshift
• Google BigQuery
• dbt
• Informatica
• Matillion

AI / Machine Learning

• Machine Learning Pipelines
• Feature Engineering
• Model Training Pipelines
• Model Deployment
• Model Monitoring
• MLOps
• MLflow
• Kubeflow
• SageMaker
• Azure ML
• Vertex AI

Generative AI

• OpenAI APIs
• Azure OpenAI
• AWS Bedrock
• LangChain
• LlamaIndex
• Vector Databases (Pinecone, FAISS, ChromaDB)
• RAG (Retrieval-Augmented Generation)
• Prompt Engineering
• AI Agents
• MCP (Model Context Protocol)

Streaming Technologies

• Kafka
• Spark Streaming
• Azure Event Hub
• AWS Kinesis

Databases

• SQL Server
• PostgreSQL
• Oracle
• MongoDB
• Cassandra
• NoSQL
• Cosmos DB

DevOps / CI/CD

• Azure DevOps
• GitHub Actions
• Jenkins
• GitLab CI/CD
• Docker
• Kubernetes
• Terraform

Data Governance

• Collibra
• Alation
• Microsoft Purview
• Apache Atlas
• Data Lineage
• Metadata Management
• Data Catalog

BI & Analytics

• Power BI
• Tableau
• Looker

Leadership Responsibilities

• Lead and mentor a team of Data Engineers and ML Engineers.
• Drive enterprise AI and Data Engineering strategy.
• Define data architecture standards and engineering best practices.
• Conduct architecture and code reviews.
• Collaborate with enterprise architects, business leaders, and product owners.
• Lead Agile ceremonies, sprint planning, and technical estimations.
• Drive continuous improvement initiatives across data engineering and AI platforms.
• Ensure security, scalability, reliability, and governance of enterprise data assets.

Required Qualifications

• Bachelor''s or Master''s degree in Computer Science, Data Science, Information Systems, Engineering, or a related field.
• 12+ years of Data Engineering experience.
• 8+ years of Python and SQL development.
• 6+ years of PySpark and Spark development.
• 5+ years of Snowflake or Databricks experience.
• 5+ years of cloud platform experience (Azure, AWS, or Google Cloud Platform).
• 4+ years of AI/ML data engineering experience.
• Strong experience developing enterprise ETL/ELT pipelines.
• Experience implementing Lakehouse architectures.
• Hands-on experience with MLOps platforms and AI model deployment.
• Strong understanding of Data Modeling (Star Schema, Snowflake Schema, Data Vault).
• Experience supporting enterprise AI initiatives.
• Strong Agile/Scrum experience.

Preferred Qualifications

• Experience with Large Language Models (LLMs).
• Experience building RAG-based applications.
• Experience with AI Agents and autonomous workflows.
• Knowledge of Agentic AI architectures.
• Experience with graph databases (Neo4j).
• Experience with vector search and semantic retrieval.
• SnowPro, Databricks, Azure, AWS, Google Cloud Platform, or AI/ML certifications.
• Financial Services, Banking, Healthcare, or Retail domain experience.