Data Engineer – AI

• Design, build, and optimize scalable ETL/ELT pipelines using Apache Spark and Delta Lake across AWS, Azure, and GCP
• Implement scalable Lakehouse architectures processing structured and unstructured data at enterprise scale
• Automate data ingestion, storage, and feature engineering workflows for analytics and real-time AI workloads
• Tune Spark jobs for low latency, high throughput, and cloud cost-efficiency
• Partner with ML engineers and data scientists to operationalize AI models in Databricks using MLflow
• Implement CI/CD pipelines and Infrastructure-as-Code using Terraform and Databricks CLI
• Enforce enterprise data security, access controls, and compliance standards including GDPR, HIPAA, and SOC 2
• Collaborate with client tech leaders, product managers, and engineering teams to translate AI data requirements into business outcomes
• Author technical documentation
• Lead solution handoffs to client engineering teams

• 3+ years in cloud-native data engineering
• 2+ years of dedicated, hands-on Databricks, Apache Spark, Delta Lake, and MLflow experience
• High proficiency in Python, SQL, Spark frameworks, CI/CD, and Infrastructure-as-Code (Terraform or Databricks CLI)
• Proven track record operating in fast-paced Agile/DevOps consulting environments
• Experience delivering cloud-agnostic architectures
• Exceptional client communication
• Active Databricks Certified Data Engineer (Associate or Professional) or Databricks ML Professional certification is strongly preferred

• Remote-First Working Environment – Collaborate from anywhere while staying connected with our global team.
• Collaborative, Innovative Culture – Work alongside top talent in a fast-paced, supportive environment that values curiosity and initiative.
• Equal opportunity workplace
• Reasonable accommodation for individuals with disabilities