Data Engineering & Analytics
Build robust, scalable, and audit-ready data pipelines and data warehouses. We design data systems for the messy realities of real-world business—handling schema drift, multi-supplier ingestion, and late-arriving data without silent failures.
The Challenge
Data trapped in operational silos, slow or unreliable report generation, schema changes breaking pipelines, and lack of trust in data quality.
Key Benefits
Centralized, query-optimized data warehouse (single source of truth) using Snowflake or BigQuery.
Robust ETL/ELT pipelines with explicit data contracts and automated schema validation.
Near real-time streaming data ingestion using Apache Kafka or AWS Kinesis.
Automated data quality monitoring and anomaly alerting on pipeline runs.
Self-service BI integration for business analyst enablement and reporting.
What You'll Get
Data architecture diagrams and dimensional models.
dbt project repository and ETL/ELT DAG configurations.
Centralized data warehouse deployment (Snowflake/BigQuery).
Data quality exception reporting dashboards.
BI dashboard templates (Tableau / Power BI / Looker).
Technologies We Use
Success Stories
See how we've helped clients with data engineering.
EU Auto Parts Group
Auto Parts Data Platform for a European B2B Marketplace
−45% Wrong-fit returns
Regional supermarket chain (APAC)
Inventory & Promotion Platform for a 120-Store Supermarket Chain
3 days → ~2h Promotion go-live time
Frequently Asked Questions
How do you handle schema changes from external data suppliers?
We enforce explicit data contracts using serialization schemas (like Avro or JSON Schema) at ingestion. If a supplier changes their format, the pipeline quarantines the invalid records to a dead-letter queue and alerts the team instead of failing silently.
What is the difference between ETL and ELT, and which do you use?
ETL extracts, transforms, and then loads data, while ELT loads raw data directly into the warehouse and transforms it there. We prefer ELT using dbt (data build tool) and Snowflake/BigQuery because it keeps raw data accessible and runs transformations with cloud-scale performance.
Can you help us build real-time analytics?
Yes. We build streaming architectures using Apache Kafka or AWS MSK/Kinesis combined with lightweight stream processors, enabling real-time stock updates, checkout telemetry, or instant diagnostic alerts.
Let's Discuss Your Data Engineering Project
Schedule a free consultation to explore how we can help.