What We Build
Data Pipelines
ETL/ELT workflows that extract, transform, and load data reliably. Airflow, Dagster, dbt, or custom solutions.
Data Warehouses
Snowflake, BigQuery, Redshift, or Databricks—architected for your query patterns and cost constraints.
Streaming Infrastructure
Kafka, Kinesis, Pub/Sub—real-time data flows for applications that can’t wait for batch.
Data Lakes & Lakehouses
Scalable storage layers that handle structured and unstructured data with proper governance.
Data Quality & Observability
Monitoring, validation, and alerting so you know when data is wrong before it causes problems.
Analytics Engineering
dbt models, semantic layers, and the transformation logic that turns raw data into business insights.
Technical Foundations
Batch Processing
Spark, dbt, and traditional ETL patterns for high-volume, scheduled data movement.
Stream Processing
Kafka Streams, Flink, Spark Streaming for real-time analytics and event-driven architectures.
Orchestration
Airflow, Dagster, Prefect—workflow management that handles dependencies and failures gracefully.
Data Modeling
Dimensional modeling, data vault, or whatever approach fits your analytical needs.
How We Engage
Data Discovery
Understanding your data sources, current state, and what questions you need to answer.
Architecture
Designing the target data platform with clear trade-offs around cost, latency, and complexity.
Build
Implementing pipelines, models, and infrastructure—usually iteratively with your team.
Validation
Data quality checks, reconciliation, and testing to ensure the numbers are right.
Documentation
Data dictionaries, lineage, and operational runbooks for long-term maintainability.
Enablement
Training your team on tools, patterns, and best practices.
When to Call Us
Your data team spends more time fixing pipelines than building new ones
We'll stabilize your infrastructure, add observability, and establish patterns that reduce maintenance burden.
You're building your first real data platform
We'll help you avoid the common mistakes and design something that scales with your needs.
Analytics queries are too slow or too expensive
We'll optimize your data models, query patterns, and infrastructure to balance performance and cost.
Data quality issues are eroding trust
We'll implement validation, monitoring, and alerting that catches problems before they reach dashboards.
Frequently Asked Questions
Should we build a data lake or a data warehouse?
+Probably both, in the form of a lakehouse. But it depends on your use cases. We’ll help you understand the trade-offs and design something appropriate for your actual needs, not an architecture diagram from a vendor.
How do you handle data governance and compliance?
+We build governance into the architecture—access controls, audit logging, data lineage, and retention policies. For regulated industries, we’ve implemented GDPR, HIPAA, and financial compliance requirements.
What about real-time analytics?
+Real-time adds significant complexity and cost. We’ll help you determine if you actually need sub-second latency or if near-real-time (minutes) is sufficient. Often the business requirement is less stringent than initially assumed.
Can you work with our existing tools?
+Yes. We’re tool-agnostic and will work with whatever you have. That said, we’ll be honest if we think a different approach would serve you better.