Overview
Data is only valuable when it is accessible, trustworthy and actionable. Our data engineering practice builds the modern data stack: cloud data warehouses (Snowflake, BigQuery, Redshift, Databricks), ELT pipelines (dbt, Airflow, Dagster, Prefect), data lakes (Delta Lake, Iceberg, Hudi), streaming (Kafka, Flink, Kinesis) and reverse ETL (Census, Hightouch).
We implement data governance — catalogues (DataHub, Amundsen), contracts, lineage, quality checks (Great Expectations, dbt tests) and access control. Our BI team delivers executive dashboards, self-serve analytics (Metabase, Superset, Looker) and embedded analytics for your products.
How AI Powers This Service
AI-augmented data engineering:
• Automated Schema Evolution — AI detects and handles schema changes
• Natural-Language SQL — text-to-SQL for self-serve analytics
• Data Quality ML — anomaly detection on freshness, volume and distribution
• Lineage Automation — AI infers column-level lineage from queries
• Semantic Layer — AI-generated metrics definitions and business glossary