Reliable pipelines that run in production — not just in demos.
We build the plumbing that every analytics project depends on: ingestion, transformation, orchestration, and storage. Using Airflow, dbt, Spark, and AWS Glue. Monitored, tested, version-controlled, and documented.
What we build
- Ingestion pipelines from databases, REST APIs, flat files, and streaming sources (Kafka)
- dbt transformation layers with tests, documentation, and lineage tracking
- Airflow or Prefect DAGs for orchestration with alerting on failure
- Data lakes on S3 or GCS with Delta Lake or Apache Iceberg for ACID transactions
- AWS Glue, Google Dataflow, or Azure Data Factory for managed ETL at scale
- Apache Spark jobs for large-scale batch processing and feature engineering
- Data quality checks with Great Expectations embedded in the pipeline
- CI/CD for data pipelines: automated testing on every PR before deployment
How we work
Audit your current data landscape
We catalogue every data source, its format, volume, freshness, and quality. You will know exactly what you have before we write a single transformation.
Design the architecture
We choose the right storage layer (data warehouse vs. data lake vs. lakehouse), the right orchestration tool, and the right transformation approach for your scale and budget.
Build incrementally
We deliver working pipelines in two-week sprints — not a big-bang deployment. Each sprint adds a tested, monitored layer that the business can already use.
Add observability
Every pipeline gets alerting, logging, and data quality checks. We use Monte Carlo, re_data, or Great Expectations depending on the stack.
Document and hand over
We document every pipeline, transformation, and data contract. We run knowledge-transfer sessions so your team can operate and extend what we built.
Frequently asked questions
We already have some pipelines. Can you improve them instead of rebuilding?+
Yes — and that is usually the right call. We audit what exists, identify the reliability and performance bottlenecks, and propose a prioritised improvement plan. A full rebuild is rarely necessary.
How do you handle schema changes from source systems?+
We design pipelines with schema evolution in mind using tools like Delta Lake and Avro. We also set up automated schema drift alerts so you know immediately if an upstream system changes a column without warning.
What cloud platforms do you work with?+
AWS (Glue, Redshift, S3, Lambda, EMR), Google Cloud (BigQuery, Dataflow, GCS, Composer), and Azure (Data Factory, Synapse, ADLS, Databricks). We recommend the right platform for your existing environment and team skills.