Data Engineering
Data Engineering Services
Pipelines, Warehouses & Real-Time Streaming — Built to Be Trusted
We build the data infrastructure your analytics and AI initiatives depend on. Reliable pipelines, well-modelled warehouses, real-time streaming, and the DataOps practices that keep it all trusted and maintained.
What We Build
Data Warehouse Design & Build
Architect and implement cloud data warehouses on Snowflake, BigQuery, or Redshift. Dimensional modelling, slowly changing dimensions, and star-schema design built for analytics performance.
ETL/ELT Pipeline Development
Build reliable data pipelines that ingest from SaaS tools, databases, APIs, and event streams. dbt transformations, incremental loads, and automated data quality checks built in.
Real-Time Streaming
Design event-driven data architectures using Apache Kafka, AWS Kinesis, or Pub/Sub. Sub-second latency for operational analytics, fraud detection, and real-time dashboards.
Data Platform Modernization
Migrate from legacy warehouses and brittle ETL scripts to a modern lakehouse or cloud-native data platform. Maintain business continuity throughout the migration.
Analytics Engineering
Build a semantic layer your analysts can trust: dbt models, metric definitions, and documentation that turns raw warehouse tables into business-ready data products.
DataOps & Data Quality
Automated data quality checks (Great Expectations, dbt tests), pipeline monitoring, alerting on data freshness and schema changes, and full data lineage visibility.
Data Pipeline Development
End-to-end data pipeline development: ingestion, transformation, validation, and delivery. We build pipelines that are observable, testable, and idempotent — whether batch (dbt + Airflow) or real-time (Kafka + Flink). Production-grade from day one, not held together with cron jobs.
Data Warehouse Development
Data warehouse development on Snowflake, BigQuery, or Redshift: schema design, dimensional modelling, dbt transformation layers, and BI-ready data marts. We take you from raw source tables to a governed, documented warehouse your analysts trust.
Common Engagements
Our Data Engineering Stack
Warehouses
Snowflake, BigQuery, Redshift
Transformation
dbt, Spark, pandas
Orchestration
Apache Airflow, Prefect, Dagster
Streaming
Kafka, Kinesis, Pub/Sub
Ingestion
Fivetran, Airbyte, custom connectors
Data Quality
Great Expectations, dbt tests, Monte Carlo
Frequently Asked Questions
Ready to Build a Reliable Data Foundation?
Tell us your current data stack and what's breaking down. We'll review it and propose a target architecture — in a free 45-minute discovery call.
Also see: AI & ML Solutions · Cloud & DevOps · Application Modernization