Production-grade pipelines, lakehouse architectures, and data infrastructure designed for scale, reliability, and governance.
Modular, cloud-native data platforms built for high-throughput ingestion, governed transformation, and low-latency serving.
Vetted, open-source-first tooling with enterprise support paths. We standardize on platforms that maximize developer velocity and operational stability.
Our engineering methodology ensures predictable delivery, rigorous testing, and continuous optimization.
Map data sources, latency requirements, compliance constraints, and existing infrastructure gaps.
Blueprint storage layers, processing paradigms, security boundaries, and cost optimization strategies.
Build idempotent, version-controlled pipelines with schema validation, retry logic, and dead-letter handling.
Document runbooks, configure alerting, establish SLOs, and transfer ownership with full observability.
Baseline capabilities delivered across all data engineering engagements.
| Capability | Standard | Advanced | Notes |
|---|---|---|---|
| Data Ingestion | Batch & CDC | Real-time Streams (Kafka/Pulsar) | Schema evolution handled automatically |
| Processing Model | Lambda / Kappa | Unified Streaming | Stateful processing with exactly-once semantics |
| Storage Layer | Object Storage + DW | Lakehouse (Delta/Iceberg) | ACID transactions & time travel enabled |
| Orchestration | Airflow / Prefect | Dagster + Temporal | Full DAG visualization & backfill support |
| Quality & Testing | Unit & Integration | Data Contracts & SLOs | Automated regression testing on deploy |
| Security & Compliance | Encryption & RBAC | Row/Col Level + Audit Logs | SOC 2, GDPR, HIPAA ready patterns |
Whether you're modernizing legacy ETL, building a real-time lakehouse, or establishing data governance, our engineering team delivers production-ready infrastructure.