Data Engineering & ETL
Data pipelines and warehouses your analysts can actually trust
We build data pipelines, warehouses, and transformation layers that move your operational data into analytics-ready products. You get documented data contracts, quality checks that catch problems before your dashboards do, and lineage that tells you exactly where every number came from.
What we build
Every business runs on data, but raw operational data is rarely in the shape that answers business questions. Tables that work fine for a transaction system are poorly suited for the question "which customers are likely to churn this month?" We build the layer between your operational systems and your analysts: pipelines that extract data reliably, transform it into well-defined data products, and load it into a warehouse or lakehouse where your team can query it with confidence. Real outcomes we have delivered: a retail analytics platform consolidating sales, inventory, and logistics data from six source systems into a single warehouse with sub-hourly freshness, a financial reporting pipeline replacing manual spreadsheet exports with automated, audited data products, and a customer data platform unifying behavioural events, CRM records, and billing data into a single customer view. The principle: every column has a definition, every pipeline has a test, and when a number changes unexpectedly, you know why.
Capabilities
- ELT pipelines — extract data from operational databases, SaaS APIs, event streams, and files; load it to your warehouse with schema evolution handled automatically.
- Data warehouse design — dimensional models, data vault patterns, or wide tables depending on your query patterns and team's SQL skill level — chosen to match your actual use case.
- Transformation layers — dbt models with tests, documentation, and lineage so every derived metric is traceable back to its source data and every breaking change is caught in CI.
- Streaming and batch — near-real-time pipelines for operational dashboards alongside batch processing for heavy analytical workloads, with clear SLAs per data product.
- Data quality monitoring — freshness checks, row count validation, distribution drift detection, and alerting so your team finds out about data problems before your users do.
- Data contracts — documented agreements between data producers and consumers so a schema change in an upstream system does not silently break a downstream dashboard.
- Orchestration — pipeline scheduling, dependency management, retries, and alerting with visibility into what ran, what failed, and why.
- Reverse ETL — push warehouse-computed insights back into your CRM, marketing tools, or product database to operationalise analytics results.
Stack
- Ingestion: Airbyte, Fivetran, custom connectors for internal APIs, Kafka and Kinesis for streaming sources
- Transformation: dbt Core, dbt Cloud, SQL, Python for complex transformations
- Warehouses: BigQuery, Snowflake, Redshift, ClickHouse for high-frequency event data, DuckDB for lightweight local analytics
- Orchestration: Apache Airflow, Dagster, Prefect, dbt Cloud scheduler