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Service Line — Data Engineering

The data foundation
that makes AI actually work.

Data engineering is the unglamorous work that makes everything else possible. Without reliable, well-governed data pipelines, your AI is fiction and your analytics are guesswork. We build data foundations that last.

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// What we deliver

Pipelines that are reliable, observable, and production-grade.

Data Lakehouse Architecture
Unified storage and compute that serves both analytics and ML workloads. Delta Lake, Apache Iceberg, and Databricks implementations at petabyte scale.
Real-time Streaming Pipelines
Event-driven architectures processing millions of events per second — Apache Kafka, Flink, and Kinesis for sub-100ms latency analytics and ML feature computation.
Batch & ELT Pipeline Engineering
Robust dbt transformation layers, Airflow orchestration, and data quality frameworks that make batch processing reliable enough for production AI.
Data Mesh Implementation
Domain-driven data ownership, federated governance, and self-serve data infrastructure that scales without creating central bottlenecks.
Data Governance & Cataloguing
Apache Atlas, Collibra, and DataHub implementations that give your data estate discoverability, lineage, and access control at scale.
Cloud Data Warehousing
Snowflake, BigQuery, and Redshift implementation with performance optimisation, cost controls, and automated data quality monitoring.

// Client outcomes

Numbers that move
the business.

85%
Faster time-to-insight after lakehouse implementation
99.9%
Pipeline reliability SLA we maintain
60%
Reduction in data infrastructure costs
10x
Increase in self-serve analytics adoption

Technologies we use

Apache KafkaApache FlinkApache SparkDatabricksDelta LakeApache IcebergdbtAirflowPrefectSnowflakeBigQueryRedshiftDataHubGreat Expectations

Ready to get started?

Book a discovery call and we'll scope your project, validate the approach, and give you a realistic roadmap — within a week.

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