Big Data

Big Data Platforms & Infrastructure

We unify data engineering, cloud warehousing, and scalable data platform design into one big data service. From ingestion and transformation to storage, modeling, and performance optimization, we build the infrastructure that powers dependable analytics at scale.

What We Deliver

• Data pipeline design and implementation using Airflow, Dagster, dbt, Spark, and modern ELT patterns
• Cloud warehouse and lakehouse architecture on Snowflake, BigQuery, Redshift, Delta Lake, or similar platforms
• Dimensional modeling and transformation layers that turn raw data into trusted analytical datasets
• Real-time and batch processing foundations for workloads ranging from recurring reporting to large-scale event processing
• Performance, cost, and reliability optimization across compute, storage, orchestration, and query workloads
• Governed self-service foundations with testing, documentation, naming standards, and access controls

Our Approach

We treat big data systems as production platforms, not one-off technical projects. That means version control, CI/CD, testing, documentation, observability, and architecture decisions that reflect the scale and maturity of your business.

We also balance ambition with pragmatism. Not every company needs a heavyweight stack, but every company does need reliable foundations. We combine the best of data engineering and warehousing into solutions that are scalable, maintainable, and ready for business use.

Technologies

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