Data Engineering

Modern Data Pipelines & Infrastructure

Robust data pipelines that move, transform, and deliver data reliably at any scale. Whether you are migrating from legacy ETL or building greenfield on the cloud, we engineer infrastructure that your team can trust and maintain.

What We Deliver

• Data pipeline design and implementation using Airflow, Dagster, dbt, and Spark
• Cloud data warehouse setup on Snowflake, BigQuery, or Redshift with optimized schemas and cost controls
• Real-time streaming architectures with Kafka, Kinesis, or Pub/Sub for low-latency use cases
• Data lake and lakehouse implementations on Delta Lake, Iceberg, or Hudi
• Legacy migration from on-premise systems to cloud-native architectures
• Data quality frameworks with automated testing, monitoring, and alerting

Our Approach

We treat data pipelines as production software. That means version control, CI/CD, automated testing, documentation, and monitoring. A Jupyter notebook that "just runs" is not a pipeline — it is technical debt.

We also believe in right-sizing architecture. You do not need Kubernetes and Spark for 10 GB of data. We match the technology to the scale of the problem, avoiding over-engineering that increases cost and complexity without delivering value.

Technologies

Apache Airflow, dbt, Apache Spark, Kafka, Fivetran, Airbyte, Dagster, Prefect, Snowflake, BigQuery, Redshift, Delta Lake, Docker, Terraform, GitHub Actions.