Technologies
Cross-Cloud, Not Single-Vendor
We work across the platforms enterprise data estates actually run on, and stay vendor-neutral about which one is right for you.
Cloud Platforms
Microsoft Azure
Primary cloud platform for enterprise data and AI workloads, including Azure Data Factory and Azure OpenAI.
AWS
S3, Redshift, and Bedrock-based architectures for AWS-native and multi-cloud clients.
Microsoft Fabric
Unified analytics platform for clients standardizing on the Microsoft data stack.
Data Platforms
Snowflake
Cloud data warehouse implementation, performance tuning, and cross-cloud data sharing.
Databricks
Lakehouse architecture, Delta Lake, and Spark-based transformation pipelines.
Azure Synapse
Integrated analytics for clients consolidating warehousing and big data processing.
Amazon Redshift
Warehouse implementation and optimization for AWS-native analytics estates.
Data Engineering
Apache Airflow
Orchestration for batch and event-driven data pipelines.
dbt
Version-controlled, tested SQL transformation layer for the modern data stack.
Apache Spark
Distributed processing for large-scale batch and streaming transformations.
Apache Kafka
Real-time event streaming and change-data-capture pipelines.
AI & ML
Python
Primary language for data engineering, ML, and AI workload development.
PyTorch
Model training and fine-tuning for custom ML and deep learning workloads.
Hugging Face
Model hosting, fine-tuning, and evaluation for open-source LLMs.
LangChain
Orchestration framework for RAG pipelines and agentic AI systems.
Azure OpenAI
Enterprise-grade LLM access with private networking and compliance controls.
AWS Bedrock
Managed foundation model access for AWS-native generative AI solutions.
BI & Analytics
Power BI
Enterprise dashboarding and semantic modeling, primary BI tool for Microsoft-stack clients.
Tableau
Visual analytics implementation for clients standardized on Tableau.
Looker
Governed, code-based semantic modeling with LookML for embedded analytics.
DevOps & Infrastructure
Terraform
Infrastructure as code across Azure, AWS, and multi-cloud environments.
Docker
Containerization for reproducible data and ML workloads.
Kubernetes
Orchestration for containerized pipelines and model-serving infrastructure.
GitHub
Source control and collaboration across engineering teams.
Azure DevOps
CI/CD pipelines for Microsoft-stack client environments.
GitHub Actions
CI/CD automation for testing, deployment, and infrastructure changes.
Databases
PostgreSQL
Operational and analytical relational workloads.
SQL Server
Enterprise relational database for Microsoft-stack environments.
MongoDB
Document-oriented storage for semi-structured application data.
Cosmos DB
Globally distributed database for low-latency, multi-region applications.