Platform architecture
Lakehouse design on open table formats with clear raw, curated and serving layers.
Data Engineering
Turn raw data into a reliable data platform.
Batch and streaming data platforms on modern lakehouse architecture — trustworthy, governed and ready for analytics and AI.
Sound familiar?
- “Every department has its own numbers and nobody trusts the dashboards.”
- “Nightly batch jobs break and reports arrive late.”
- “You want AI, but your data is scattered across silos.”
Capabilities
- Data lakes
- Data warehouses
- ETL/ELT
- Streaming pipelines
- Kafka
- Spark
- Flink
- Databricks
- Iceberg
- Data quality
- Data governance
What we deliver
How we approach Data Engineering.
Ingestion
CDC, API, file and event ingestion with schema evolution handled deliberately.
Streaming pipelines
Kafka and Flink pipelines for real-time use cases with defined latency and delivery guarantees.
Transformation
Tested, version-controlled ELT models with lineage and documentation.
Data quality
Contracts, validation checks, freshness SLAs and alerting on the data itself.
Governance
Catalog, access control, PII handling and cost visibility per domain.
AI, built in
Feature pipelines, embeddings and vector stores treated as first-class data products — versioned, monitored and governed.
Typical stack
- Spark
- Kafka
- Flink
- Databricks
- Apache Iceberg
- Hudi
- Trino
- Airflow
- dbt
Related solutions
Where this fits.
Start a conversation
Let's talk about your Data Engineering needs.
Tell us what you're building, what isn't working, or where you want to go next. A senior engineer will get back to you — not a sales script.