Data Engineering
We build the data infrastructure that makes analytics, ML, and business intelligence reliable and scalable. From modern data lakehouse architectures and real-time streaming platforms to semantic layers and self-serve analytics tooling, our data engineering practice turns fragmented data estates into trusted, queryable assets.
Key Benefits
Modern data lakehouse design: Delta Lake, Apache Iceberg, Apache Hudi on S3/GCS
Streaming data platforms: Apache Kafka, Apache Flink, Kinesis Data Streams, Pub/Sub
dbt project architecture, semantic layer design & data contract enforcement
Data catalog & lineage: Apache Atlas, OpenMetadata, DataHub, Collibra
Cloud-native warehousing: Snowflake, BigQuery, Redshift & cost optimization
Real-time OLAP: Apache Druid, ClickHouse, Tinybird
Data mesh implementation: domain ownership, data product design & federated governance
Our Process
Data Platform Assessment
We audit your current ingestion, storage, and consumption layers to identify reliability gaps, query performance bottlenecks, and ungoverned data flows.
Architecture Design
We design the target platform architecture — medallion lakehouse, streaming topology, or data mesh — selecting technologies to match your team's operational maturity and cost constraints.
Build & Model
We build ingestion pipelines, write layered dbt models (staging/intermediate/mart), enforce data contracts, and configure CI/CD for the data platform itself.
Governance, Documentation & Enablement
We populate data catalogs, define ownership and SLO agreements per data product, and train analytics and engineering teams to operate and extend the platform independently.