What We Build With It
We engineer robust data platforms and pipelines that empower your organization to make data-driven decisions.
Cloud Data Warehouses & Lakehouses
A central, scalable source of truth for all your business data, optimized for performance and cost.
Production-Grade ETL/ELT Pipelines
Reliable, scheduled pipelines that ingest data from SaaS tools, databases, and APIs, ready for analytics.
Real-Time Streaming Architectures
For use cases like real-time analytics, fraud detection, and operational monitoring, ensuring data freshness.
Data Quality & Governance Platforms
Systems that automatically test, document, and monitor your data to build and maintain trust.
Self-Service Analytics Platforms
Empowering your business users to safely and easily explore data without needing to be SQL experts.
Master Data Management & Identity Resolution
Unifying fragmented customer and product data across systems to create a single, high-confidence view of your entities.
Why Our Approach Works
We apply rigorous software engineering principles to data, ensuring your data pipelines are treated as critical, production-grade systems.
Data as a Product Mentality
Each dataset we produce has a clear owner, a defined schema, and a service-level agreement (SLA), treating data as a critical business asset.
Analytics Engineering Best Practices
We use software engineering best practices—like version control (Git), CI/CD, and automated testing—to manage our data pipelines, bringing rigor and reliability.
Built-in Data Observability
A 'black box' pipeline is a failing pipeline. We instrument our systems with logging, monitoring, and lineage tracking, so you always know the state and quality of your data.
Our Go-To Stack for Data Engineering
We build modern data platforms using best-in-class, cloud-native tools that are designed for scale and reliability.
Languages
Python & SQL for robust data processing and transformation.
Cloud Data Platforms
AWS, GCP, Azure data services (e.g., Kinesis, Dataflow, EMR, Glue).
Orchestration
Dagster, Prefect, Apache Airflow for reliable workflow automation.
Processing
Apache Spark, Flink for large-scale data transformation and streaming.
Data Storage
Data lakes, data warehouses (Snowflake, BigQuery), and lakehouses (Databricks Delta Lake).
Data Quality
Great Expectations, Monte Carlo for automated data validation and monitoring.
Frequently Asked Questions
What is dbt and why do you use it so much?
+dbt (Data Build Tool) is a transformation tool that lets us apply software engineering best practices to data modeling, enabling modular, testable, and version-controlled SQL-based data pipelines. It’s a core tool in the modern data stack.
ETL vs. ELT: which is better?
+For modern cloud data warehouses, ELT (Extract, Load, Transform) is typically the superior approach. We load raw data first, then use the power of the warehouse itself to perform transformations (with dbt). This is more flexible and scalable than legacy ETL.
How do you ensure our data is trustworthy?
+We use automated testing frameworks like Great Expectations to constantly validate our data pipelines. We write tests to check for freshness, nulls, uniqueness, and other quality metrics. If a test fails, the pipeline stops and alerts us, preventing bad data from reaching end-users.
What are Data Contracts and why should we use them?
+Data Contracts are formal agreements between data producers and consumers that define the schema, quality, and SLA of a dataset. They prevent ‘silent’ breaking changes in upstream systems from crashing your downstream analytics.
How do we manage the costs of Snowflake or BigQuery?
+We implement FinOps for data, setting up granular resource monitoring, automated query optimization, and strict warehouse auto-suspend policies to ensure you’re only paying for the compute you actually need.
Can you handle real-time streaming data?
+Yes. We build high-performance streaming pipelines using Kafka, Spark Streaming, or managed cloud services to deliver sub-second data availability for critical use cases like fraud detection and real-time operational metrics.