Data Engineering

Good analytics and AI start with good data infrastructure. We design and build data pipelines that move, transform, and serve your data reliably at scale — whether you are processing thousands of records or hundreds of millions. We replace brittle, ad-hoc data workflows with robust, monitored, and maintainable engineering.

What We Offer

• ETL and ELT pipeline design and implementation
• Real-time data streaming (Kafka, Kinesis, Pub/Sub)
• Data lake and data warehouse architecture
• Legacy data warehouse migration to cloud-native platforms
• Data quality monitoring, lineage tracking, and observability
• Data augmentation and feature engineering for ML workloads

How We Work

We start with a data audit: what sources exist, what transformations happen today (often manually or in spreadsheets), and what the downstream consumers — reports, dashboards, ML models — actually need. From that we design a layered architecture with clear separation between raw, curated, and consumption-ready data. We build with orchestration frameworks so pipelines are scheduled, monitored, and self-alerting.

Technologies

Apache Airflow, dbt, Spark, Flink; Snowflake, BigQuery, Redshift, Delta Lake; Kafka, AWS Kinesis; Python for transformation logic; Terraform and CloudFormation for infrastructure provisioning.

Talk to us about your data infrastructure challenges.