Technical expertise engineered for mission-critical, petabyte-scale data systems
We blend battle-tested distributed systems engineering with bleeding-edge generative AI models to deliver performant, resilient, and sovereign intelligence ecosystems. Across all 6 disciplines, our staff engineers adhere to strict software engineering rigor, test automation, and FinOps efficiency.
Discuss Your Technical ArchitectureData Transformation & Declarative Modeling
Raw enterprise data is messy, duplicate-heavy, and inconsistently typed. We implement declarative data modeling frameworks using dbt Core, SQLMesh, and SQLX that transform raw operational inputs into clean, audited, and analytics-ready dimensional tables.
By adhering strictly to the Medallion architecture (Bronze raw ingestion, Silver cleansed deduplication, Gold business KPIs), we ensure every metric is mathematically verifiable, fully lineage-mapped, and isolated from breaking schema modifications.
- ✓ Automated data lineage & interactive documentation graphs
- ✓ Continuous integration unit tests on all pull requests & schema changes
- ✓ Medallion lakehouse modeling with zero-copy table clones
Modern Cloud Lakehouse Platforms
Modernizing traditional on-prem data warehouses (Oracle, Teradata, SAP) into elastic, petabyte-scale cloud lakehouses built on Snowflake, Databricks Delta Lake, and Apache Iceberg with physical separation of compute and object storage.
Our platforms feature multi-cluster compute isolation that prevents long-running data science notebook queries from degrading executive reporting dashboards, complete with automated FinOps sleep triggers and query cost attribution.
- ✓ Multi-cluster compute isolation eliminating warehouse contention
- ✓ Zero-copy cloning for instantaneous, zero-cost staging testing
- ✓ Automated FinOps budget alerts and auto-suspend guardrails
Business Intelligence & Semantic Layers
Eliminate conflicting metrics across executive teams. When Marketing defines CAC differently than Finance, organizational paralysis follows. We build centralized semantic layers (Cube.js, Looker LookML, dbt MetricFlow) that standardize metrics once and serve them universally.
Whether delivering sub-second dashboards via Power BI, Tableau, or custom React executive portals, your teams query vetted, pre-aggregated Gold data with row-level security and automated cache invalidation.
- ✓ Sub-second interactive dashboard response times via pre-aggregations
- ✓ Role-based row and column level security enforcement
- ✓ Automated scheduled executive reporting distributions via email & Slack
Enterprise AI, LLMs & Autonomous Agents
Moving AI from experimental prototypes into high-throughput production requires robust data pipelines. We architect sovereign Retrieval-Augmented Generation (RAG) engines, real-time vector embeddings, and autonomous agentic workflows directly linked to your private lakehouse.
We deploy self-hosted open-source foundation models (Llama 3, Mistral, DeepSeek) inside your private VPCs using vLLM and Triton Inference Server, ensuring zero customer data egress and sub-50ms latency.
- ✓ Sovereign RAG pipelines with hybrid keyword-vector search
- ✓ Real-time inference pipelines with automated fallback and load balancing
- ✓ Model drift detection and automated rollback controls
Cloud Infrastructure & FinOps Optimization
Cloud computing spend is one of the fastest growing line items on the enterprise balance sheet. We design infrastructure as code using Terraform that enforces strict architectural FinOps from the ground up.
We conduct deep query cost profiling, eliminate Cartesian product scans, implement auto-tiering to S3 Glacier/GCS Coldline, and configure multi-cluster auto-scaling that scales down to zero when inactive—consistently reducing warehouse bills by 30% to 50%.
- ✓ Declarative Terraform modules for AWS, GCP, and Azure data estates
- ✓ Granular query cost attribution tagging per department and model
- ✓ Continuous compute rightsizing and reserved capacity planning
Automated Data Orchestration & MLOps
Fragile cron jobs and manual pipeline restarts cost engineering teams thousands of hours in unproductive triage. We architect event-driven, self-healing data orchestration DAGs using Dagster, Apache Airflow, and Prefect with automated retries and dead-letter queues.
Our MLOps pipelines automate feature store updates, scheduled model retraining, concept drift detection, and canary rollouts—ensuring that machine learning models in production maintain peak accuracy over time without human intervention.
- ✓ Self-healing pipeline orchestration with automatic exponential backoff retries
- ✓ Real-time data quality assertions with Great Expectations and Monte Carlo
- ✓ Continuous ML model evaluation, drift alerts, and automated canary deployment