The stack behind data your teams can trust.
Snowflake or BigQuery, dbt for transformation, Airflow for orchestration, and the governance frameworks that make analytical output trustworthy.
Snowflake or BigQuery, dbt for transformation, Airflow for orchestration, and the governance frameworks that make analytical output trustworthy.
Our data practice is built on the modern data stack, cloud-native warehouses, SQL-based transformation with dbt, and enterprise BI platforms, with the data governance and quality frameworks that make analytical output trustworthy. We integrate AI and ML capabilities directly into data platforms where the use case warrants it, rather than treating data engineering and AI as separate practices.
Snowflake and Google BigQuery cover most of our cloud data warehouse implementations, selected based on a client's existing cloud provider and data volume, and Databricks handles lakehouse architectures where structured and unstructured data need to live in one platform. Amazon Redshift and Azure Synapse come in for clients already committed to those ecosystems, and DuckDB is our tool of choice for fast, local analytical workloads.
dbt is the single highest-leverage tool in our data stack, bringing version control, testing, and self-documenting lineage to SQL transformation logic that used to live in untracked scripts. Apache Airflow orchestrates the pipelines around it, with Prefect or Dagster used where a client's team prefers a more Python-native orchestration model.
Fivetran and Airbyte cover the vast majority of our ingestion needs, between them supporting hundreds of SaaS sources without any custom integration code to maintain. For cloud-native pipelines we use AWS Glue, Azure Data Factory, or Google Dataflow, and Debezium handles change data capture when a client needs near real-time replication from an operational database.
Power BI and Tableau are our standard enterprise BI platforms, and the work that makes them actually useful is the semantic model design underneath, not just the dashboards on top. Looker fits well where a client wants governed, code-defined metrics, and Metabase or Apache Superset cover lighter-weight, self-service BI needs.
dbt tests and Great Expectations validate data at every pipeline stage, catching quality issues before they reach a dashboard or a model. For governance and lineage at a larger scale, we implement OpenMetadata or DataHub, and Monte Carlo adds data observability that flags anomalies a static test wouldn't catch.
Apache Kafka and Apache Flink handle the majority of our streaming and real-time processing work, with AWS Kinesis or Google Pub/Sub used when a client's pipeline is already built on that cloud's native services. Materialize and Apache Pinot come in for use cases that need queryable, low-latency views over streaming data.
Version control, testing, and self-documenting lineage for SQL transformation logic that used to live in scripts.
Snowflake or BigQuery as the warehouse, Airflow or Prefect for orchestration, selected by data volume and provider.
Anomaly detection, natural language queries, and model outputs feed back into the platform your teams already trust.
As cloud data warehouses, selected based on existing cloud provider, data volume, and cost requirements
For all SQL transformation logic, version-controlled models, automated data quality tests, and self-documenting lineage
For pipeline orchestration, scheduling, monitoring, alerting, and retry logic for all data workflows
For managed and open-source ELT connectors, covering 300+ SaaS sources with no custom integration maintenance
For enterprise BI deployment, semantic model design, report development, and self-service enablement
Using dbt tests, Great Expectations, or Soda Core to validate data at every pipeline stage