Data & Cloud Engineering Services
Core Expertise
Data Engineering
- Data pipeline development (ETL/ELT workflows)
- Real-time streaming architectures
- Data lake design and implementation (medallion architecture)
- Data quality frameworks and validation
Cloud Infrastructure
- AWS: Redshift, S3, Glue, EMR, Lambda, Kinesis, Step Functions, Athena
- GCP: Cloud Storage, Cloud Run, Cloud Spanner, BigQuery, Kubeflow
- Container orchestration with Kubernetes
- Infrastructure automation and CI/CD
Machine Learning Platforms
- MLOps pipelines and model deployment
- Training infrastructure optimization
- Feature engineering and data preparation
- Model serving (REST APIs, batch inference)
Platform Engineering
- API development (REST, GraphQL, gRPC)
- Microservices architecture
- Monitoring and observability
- Performance optimization
Technology Stack
Languages: Python, SQL, Go
Data Tools: Palantir Foundry, DBT, PySpark, Polars, Pandas, NumPy, Scikit-learn, TensorFlow, MXNet
Infrastructure: Kubernetes, Docker, AWS, GCP
Industry Experience
- eCommerce & Retail
- Financial Services
- Agriculture & Commerce
- Sustainable Mobility
- Technology & Security
Select Projects
Product Data Ingestion - Built scalable data pipelines in Palantir Foundry for eCommerce client, implementing quality checks with PySpark/Polars transforms
Financial Data Platform - Architected data lake using GCP Cloud Storage, deployed API with Cloud Run and Spanner, maintained ML workflows with Kubeflow
CRM/ERP Integration - Data modeling for corporate systems using DBT, Golang migrate, medallion architecture patterns
IoT Analytics Platform - Optimized big data pipelines on AWS (Redshift, S3, Kinesis), built IoT monitoring for physical infrastructure
Contact: software@apmac.us