Data Engineering.

Data Pipelines and Integration

We build flexible, production-grade data pipelines optimised for your workload, integrating batch, streaming and hybrid flows from databases, SaaS tools, IoT devices and enterprise applications into your cloud or on-prem data platforms. ETL/ELT pipeline design, real-time streaming with Kafka, Striim, Spark Streaming and Kinesis, and the orchestration that makes pipelines maintainable at scale.

Our pipeline engineering work covers source-system integration, transformation logic in dbt, Spark or hyperscaler-native tooling, error handling and data quality enforcement, monitoring and alerting, and the observability layer that lets your team trust what is moving through the pipelines in production.

Data Warehouses and Data Lakes

Production-grade warehouses and lakes designed to fit your workload patterns and scale, architected for query performance. Snowflake, Databricks, BigQuery, Redshift and others. Security, governance and access control baked in, with self-service through SQL, BI tools or notebooks while compliance policies are met.

Architecture decisions matter here. Lakehouse architecture (Databricks-style) versus warehouse architecture (Snowflake-style) versus separated lake-and-warehouse architecture each have trade-offs. We bring an opinionated view based on your workload pattern, your existing tooling, your team's skills, and your cost profile.

Multi-Tenant Data Platforms

We build the data platforms that complex enterprises actually need. Multi-tenant. Role-based. Audit-ready. With self-service onboarding for business teams, so the platform stops being an IT bottleneck.

Our airisDATA Finance Data Hub is the reference. Production at a tier-1 bank, serving product control, treasury, tax and group finance from a shared platform. Multi-tenant logical separation. AI-powered data quality alerts. RBAC enforced at the data access layer. Self-service ingestion that lets business teams onboard sources without filing IT tickets. The architecture and the operating model both transfer to other regulated industries.

Data Quality Automation

Data quality is not a process, it is engineering. We build custom validation checks, statistical profiling, ML-driven anomaly detection, and active data quality dashboards that catch issues before they reach the business.

Our airisDATA Active Data Quality system was built originally for regulatory reporting (CCAR, FR Y-9C). It includes ML-based constraint recommendation, deduplication and imputation, lineage-aware root-cause analysis for data quality exceptions, and an active learning loop that improves quality detection over time. The same system runs in production at tier-1 banks today and adapts directly to regulated reporting in healthcare, life sciences and other industries.

Data Governance and Lineage

Policy and process frameworks for data ownership, stewardship and access control. End-to-end data lineage tooling that lets your team trace any data point back to its source. Critical for regulatory reporting, audit, and AI explainability.

Specific deliverables include data catalog implementation on Collibra, Atlan, Alation or hyperscaler-native catalogs; lineage tooling that traces data from source systems through transformations to consuming reports and models; access control frameworks aligned to your security posture; and the operating model (steward roles, owner accountability, governance forums) that makes the technical implementation actually work.

Data Observability and Security

End-to-end visibility, control and security built into the pipelines. Checks, encryption, access controls and the operational tooling that lets your team see what is happening in production. Role-based access and stringent monitoring help ensure protection and audit readiness.

We work with the major data observability platforms (Monte Carlo, Bigeye, Soda, and others) and build custom observability where the off-the-shelf tools do not fit. The goal is the same: your team should know about a data issue before the business does.

DataOps

Taking cues from DevOps, we foster tightly-knit collaboration between data engineering, data science and business stakeholders through Agile delivery models. CI/CD for data pipelines. Automated testing of data transformations. Version-controlled data models. The practices that produce data products quickly and reliably.

Data Migrations and Platform Modernisation

Cloud migration of legacy data platforms. Phased transition strategy minimising business disruption through parallel-run testing, incremental data porting and modularity principles. Our expertise spans migrations from Cloudera, Hadoop, Teradata and on-prem warehouses to AWS, Azure, GCP, Snowflake and Databricks.

We have done these migrations at tier-1 bank scale, where downtime is not acceptable and parallel-run validation is not optional. The patterns we use (canary tenants, dual-write transition periods, automated reconciliation between old and new platforms) are battle-tested.

Business Intelligence and Visualisation

The reporting, dashboards and self-service analytics layer that turns data products into business decisions. Tableau, Power BI, Looker and others. We engineer BI implementations that scale, with proper semantic layers, governed metric definitions, and self-service that does not turn into chaos.

Data Engineering for AI

Feature pipelines, vector stores, training data preparation, RAG data ingestion, and the data layer that production AI specifically requires. The bridge between your data platform and your AI systems.

Our data engineering for AI work covers feature store implementation, vector database engineering (Pinecone, Weaviate, Qdrant, pgvector, or hyperscaler-native), RAG ingestion pipelines including chunking strategy and embedding generation, and the data quality and governance overlays that production AI needs on top of standard data engineering practices.