Data Engineering
Data pipelines and warehouse architecture that hold up under load
Engineers who build the pipeline layer between source systems and the reports leadership actually reads — with reconciliation and data quality checks built in, not bolted on afterward.
SnowflakeDatabricksAirflowdbtKafkaData Quality
Technology Depth
Data engineering coverage
Comprehensive ecosystem coverage backed by vetted senior principals and certified specialists.
Warehouse & Lakehouse
SnowflakeDatabricksGoogle BigQueryAmazon RedshiftAzure SynapseDelta LakeIcebergData Lake Design
Orchestration & Transformation
Apache AirflowdbtDagsterPrefectAzure Data FactoryAWS GlueSQL-based TransformationSlowly Changing Dimensions
Streaming & Integration
Apache KafkaKinesisEvent HubsDebezium (CDC)FivetranInformaticaAPI-based IngestionFile-based Ingestion
Quality & Governance
Data Quality RulesGreat ExpectationsSchema ValidationData LineageData CatalogAccess GovernanceCost MonitoringPySpark
Enterprise Delivery
Typical data engineering engagements
- Pipeline build — source-to-warehouse ingestion with scheduling and alerting
- Warehouse design — modelling that survives changing reporting needs
- Migration — legacy ETL and warehouses onto modern platforms
- Data quality — validation rules that catch bad data before it reports
- Cost optimization — warehouse compute and storage tuning
- Documentation — lineage and definitions your team can maintain
Pipelines fail silently
A pipeline that completes without error can still deliver wrong numbers. Data quality checks need to be part of the pipeline, not a separate step someone forgets to run.
- Row count and value range checks on every load
- Alerting that reaches someone who can act same day
- Reconciliation to source system totals, not just row counts
- Lineage documented so a bad number can be traced back
Common Questions
Frequently Asked Questions
Transparent insights into our delivery, pricing models, security compliance, and onboarding timelines.
Can you migrate from Informatica or SSIS to a modern stack?
Yes. Migration usually runs both pipelines in parallel with reconciliation until the new pipeline is trusted, rather than a single cutover.