Most requested
Data Pipeline Engineering
Unified ETL and ELT with real-time streaming where it earns its cost. Kafka-driven architectures, and batch schedules that finish before the morning SLA.
- KAFKA
- SPARK
- FLINK
Data platform engineering
Your pipelines are the product now.
We rebuild data platforms that have outgrown their original design, while they stay in production. Fewer moving parts, costs you can attribute, and latency measured in milliseconds instead of hours.
Four disciplines, one accountable team
Most engagements touch two or three. We scope to what your stack actually needs.
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Modern Data Warehousing
Snowflake, BigQuery, and Redshift tuned for what you actually query. Compute attribution per team, tiered storage, and a bill you can forecast.
- SNOWFLAKE
- DBT
- BIGQUERY
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MLOps & AI Infrastructure
Feature stores and serving infrastructure that training and production agree on. Skew detection in CI, so drift fails the build instead of the quarter.
- KUBERNETES
- MLFLOW
- PYTORCH
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Data Governance & Security
Column-level classification, PII masking, and lineage emitted as a build artefact. Audit evidence your CI produces instead of your engineers.
- IAM
- SOC2
- VAULT
What we work in
Managed services, self-hosted, or the hybrid you already run.
- SNOWFLAKE WAREHOUSE
- DBT TRANSFORM
- AIRFLOW ORCHESTRATION
- KUBERNETES RUNTIME
- TERRAFORM PROVISIONING
- SPARK COMPUTE
- KAFKA STREAMING
- BIGQUERY WAREHOUSE
- FLINK STREAMING
- MLFLOW ML LIFECYCLE
How the work runs
Each phase ends in something you keep, whether or not we continue to the next one.
- 01
Discovery
Stakeholder alignment and current-state stack audit.
- 02
Architecture
Formal specification of components and data flows.
- 03
Implementation
Agile deployment with CI/CD and testing suites.
- 04
Optimization
Continuous performance tuning and scaling.
Tell us what's breaking.
You get a lead architect on the call, not a salesperson. If we are not the right fit we will say so and point you somewhere better.