Databricks · Snowflake · BigQuery
Lakehouse · Streaming · Governed
Data Engineering that AI can actually use.
AI is only as good as your data. We build cloud-native data platforms on Databricks, Snowflake, BigQuery, and Microsoft Fabric — with governance, real-time pipelines, lineage, and AI-readiness from day one. Lakehouse-native, vendor-agnostic, audit-ready.
Wyoming C-Corp · Dallas HQ · 585 engineers on tap
Databricks
Snowflake · BigQuery
Real-time
streaming pipelines
Governance
by default
AI-ready
architecture
Why our data engineering
Most data warehouses can't feed AI. Ours do, from day one.
Legacy ETL + warehouse architectures weren’t designed for AI workloads. We build lakehouses that serve both BI and AI — with real-time pipelines, embedding stores, and governance baked in.
01 / LAKEHOUSE-NATIVE
Databricks · Snowflake · Fabric
One platform for BI, ML, AI. Delta Lake / Iceberg / Snowflake hybrid. AI-workload-ready from day one.
02 / GOVERNANCE BY DEFAULT
Lineage · catalog · access
Unity Catalog, Snowflake Horizon, custom governance frameworks. Lineage tracked, access controlled, audit-logged.
03 / REAL-TIME WHERE IT MATTERS
Kafka · Kinesis · Pub/Sub
Streaming pipelines for fraud detection, agentic AI feedback loops, real-time analytics. Sub-second latency targets.
04 / VECTOR + RELATIONAL
Embeddings inside
Vector DBs (Pinecone, Qdrant, Postgres + pgvector) integrated with the warehouse. Embeddings refresh on data updates. RAG-ready.
Data engineering services
From pipelines to platforms. End to end.
LAKEHOUSE BUILD
Databricks · Snowflake
Lakehouse architecture from scratch. Bronze / silver / gold layering. Delta Lake / Iceberg. Workload-tier optimization.
DATA PIPELINES
Batch + streaming
Airflow, dbt, Kafka, Spark Streaming. Idempotent, observable, retry-safe. Per-pipeline SLA monitoring.
MIGRATION
Legacy → cloud lakehouse
Strangler-pattern migrations from on-prem warehouses to cloud lakehouse. Zero-downtime cutover.
GOVERNANCE + LINEAGE
Catalog · lineage · access
Unity Catalog, Snowflake Horizon, custom governance frameworks. Column-level lineage. Access controls.
REAL-TIME
Streaming + CDC
Change-data-capture from source systems. Streaming aggregation. Real-time dashboards and alerts.
AI-READY DATA
Embeddings · feature stores
Selected Production Work
Proof. Not pitch decks.
FAQ
Questions buyers actually ask.
Databricks for AI/ML-heavy workloads (notebook UX, Spark depth, MLflow integration). Snowflake for BI-first, multi-region simplicity, governance. Microsoft Fabric for Microsoft-shop integration. We’re neutral and recommend per use case.
Yes — Teradata, Oracle Exadata, on-prem SQL Server, legacy Hadoop. Strangler-pattern migration: parallel run, gradual cutover, retire when stable. Typical timeline: 9-18 months for large estates.
Great Expectations, dbt tests, Soda, or custom validation framework. Tests run as part of every pipeline. Data quality SLAs per dataset. Anomaly detection on production traffic.
Yes — that’s the point. Embedding generation, vector indexing, feature stores, real-time event streams to agentic AI. The lakehouse serves both BI and AI from the same foundation.
Audit your data maturity.
Free data maturity assessment. We benchmark your current architecture, governance, and AI-readiness, and return a prioritized improvement list.
Data Engineering Services · Wyoming C-Corp · Dallas HQ · +1 510 850 4645 · [email protected]