Data Engineering
Modern data stacks with Snowflake, dbt, Airflow, and Kafka. We build pipelines that are actually maintained and dashboards that get used.
What we deliver
Data warehouse design
Snowflake, BigQuery, or Redshift — correctly modelled for your query patterns, with sensible clustering, partitioning, and access controls.
ELT pipelines
dbt transformation layers with modular, tested SQL. Airbyte or Fivetran for source connectors. Documented data lineage from raw source to reporting model.
Orchestration
Airflow DAGs for complex dependencies, dbt Cloud for transformation scheduling, and monitoring that alerts on failures before stakeholders notice.
Real-time data
Kafka and Flink for streaming pipelines. Change data capture from operational databases into the warehouse with sub-minute latency.
BI & dashboards
Looker, Metabase, or Superset with semantic layers, role-based access, and dashboards built around actual decision-making workflows.
Data quality & governance
Great Expectations or dbt tests for quality. Data catalogues, PII tagging, and lineage documentation for compliance.
Our stack
The tools we use on every engagement in this practice.
Things people ask us
Snowflake for most use cases — flexible compute separation, strong ecosystem, predictable pricing. BigQuery if you are already in GCP. Redshift for heavy AWS integrations.
An MVP stack — warehouse, core pipelines, and two or three dashboards — typically takes six to eight weeks. A full platform with governance takes three to four months.
Yes. We have migrated to Looker, Metabase, and Power BI multiple times. We audit your existing reports, rebuild the high-value ones, and help you sunset the rest.
PII tagging at ingestion, column-level masking in the warehouse, role-based access down to individual columns, and automated data retention policies.
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