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Modern data infrastructure and engineering practices that handle real-world complexity - from messy sources to petabyte scale.
Most engineering teams don't struggle with individual tools - they struggle with system reliability at scale. True Data Engineering builds pipelines that survive schema changes, platforms that handle traffic spikes gracefully, and architectures that evolve without breaking existing workflows. At Fornax, we engineer data systems that combine performance, reliability, and maintainability into infrastructure that scales predictably.
Our approach focuses on engineering fundamentals: idempotent pipelines that handle retries safely, monitoring that catches issues before users notice, and abstractions that reduce cognitive load for development teams. We design systems that handle the messy reality of production data while maintaining clean interfaces for consumers. The result: data infrastructure that enables continuous deployment, handles failure gracefully, and provides the foundation for real-time analytics and ML workloads that actually work in production.
How do we build pipelines that don't break when schemas change?
What's the right pattern for handling late-arriving and out-of-order data?
How do we manage backfill and reprocessing without downtime?
How do we test data pipelines and ensure data quality in CI/CD?
What's the best way to handle streaming vs. batch for different SLAs?
Event-driven, idempotent data workflows with proper error handling, dead letter queues, and automatic retry logic.
Real-time pipelines using Kafka, Pulsar, or cloud-native streaming with exactly-once semantics and windowing strategies.
Automated testing, schema validation, and data contracts with continuous monitoring and alerting.
Terraform/CDK templates for reproducible environments, auto-scaling configurations, and disaster recovery.
Distributed tracing, metrics, and logging for data pipelines with custom dashboards and intelligent alerting.
Query optimization, partitioning strategies, and caching layers that maintain sub-second latency at scale.
How we engineer your data platform
Architecture Design
Design event-driven systems with proper separation of concerns, fault tolerance, and horizontal scaling patterns.
Pipeline Implementation
Build robust ETL/ELT workflows with proper error handling, monitoring, and recovery mechanisms.
Stream Processing Setup
Implement real-time data processing with windowing, watermarks, and exactly-once delivery guarantees.
Quality Engineering
Create automated testing pipelines, schema registries, and data contracts that catch issues early.
Operations & Monitoring
Deploy comprehensive observability, alerting, and automated remediation for production reliability.
ELT-first architecture
Raw data ingestion to cloud warehouses with transformation in SQL using dbt for maximum performance and maintainability.
Git-based workflows
Version control for SQL transformations, infrastructure configs, and data contracts with proper branching and review processes.
Declarative infrastructure
Infrastructure as code using Terraform for reproducible environments across development, staging, and production.
SaaS-first resilience
Leverage managed service SLAs while implementing proper monitoring, alerting, and failover strategies.
dbt testing framework
Comprehensive data quality testing using dbt tests, macros, and custom validations that run automatically.
Modern observability
Tool-native monitoring (Snowflake Query History, dbt docs) integrated with external monitoring like DataDog or Monte Carlo.
Cost governance
Automated spend monitoring, resource scaling, and optimization across Snowflake compute, Databricks clusters, and SaaS tool usage
Explore All Capabilities
Strategy and Transformation
We help leaders build strategies that don’t sit in decks, but those that scale, adapt, and deliver measurable value.
Data Foundation
A modern data foundation gives you one source of truth for analytics, AI, and decision-making - engineered for reliability, speed, and scale.
Advanced Analytics & Insights
We build analytics platforms and production models so leaders make faster, confident decisions at scale.
AI / ML Innovation
From robust AI engineering to production-grade LLM solutions and ML platforms, Fornax turns experimentation into scalable impact.
Use dbt's package system with separate projects per domain connected through dbt hub or git submodules. Implement shared macros and testing standards through internal dbt packages. Use Snowflake's database/schema structure to isolate environments and enable cross-team data sharing through well-defined marts.
Dagster excels for complex data lineage and testing integration with dbt. Prefect offers great developer experience and dynamic workflows. Airflow works well if you need extensive operator ecosystem. Choose based on team preferences and complexity - most modern stacks work well with any of these when properly configured.
Implement usage-based alerting in Snowflake with automatic warehouse suspension. Use Databricks cluster policies and auto-termination. Monitor SaaS tool usage through APIs and set up budget alerts. Tag resources consistently for cost allocation and use tools like Vantage or CloudZero for unified cost monitoring.
Implement dbt tests at source (freshness, uniqueness), staging (not null, relationships), and mart layers (business logic validation). Use dbt-expectations for advanced statistical tests. Run tests in CI/CD with proper test data management. Combine with Great Expectations for runtime data quality monitoring.
Enable Fivetran's schema drift handling with alerts. Use dbt's source freshness and schema tests to detect changes early. Implement dbt snapshots for SCD handling. Design dbt models to be resilient to new columns using select * exclude patterns and proper source definitions with descriptions.
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