Data Engineering
AI-ready data, engineered for scale.
Modern data platforms, reliable pipelines and governance that make your data trustworthy for analytics and AI, and cheaper to run than what you have today.
Why it matters
Every AI ambition is a data project first
AI amplifies whatever data you feed it. Inconsistent definitions, missing lineage, stale pipelines and unclear access rules surface instantly when an assistant answers a customer or an agent updates a record.
We build data foundations designed for both analytics and AI: governed lakehouse or warehouse platforms, modular pipelines with testing and observability, a semantic layer business users trust, and fine-grained access controls that AI applications inherit automatically.
Typical starting points
- Migrating from Teradata, Netezza, Oracle, SQL Server or Hadoop to a modern cloud platform
- Consolidating fragmented warehouses and lakes after acquisitions
- Stabilizing brittle pipelines and reducing data platform costs
- Making data AI-ready: unstructured data processing, vector search and governed access
Solutions in this practice
Start with the problem you have
Each solution can run on its own or as part of a broader program.
What we deliver
Capabilities
Delivered by senior architects and specialist engineers, with security and governance designed in.
Platform architecture
Lakehouse, warehouse and streaming architectures on Snowflake, Databricks, Fabric and BigQuery.
Migration & consolidation
Automated code conversion, data validation and parallel runs for zero-surprise cutovers.
Pipelines & DataOps
Modular, tested, observable pipelines with dbt, orchestration and CI/CD.
Governance & access
Catalogs, lineage, policies and fine-grained access control that AI inherits.
Semantic layer & BI
Shared metric definitions and BI modernization, including analytics copilots.
Cost optimization
Workload tuning, storage tiering and consumption guardrails.
Approach
How the engagement runs
Clear phases, clear exit criteria, and a working result at every stage.
Assess
Current estate, workloads, costs, data quality and target-state options.
Design
Target architecture, migration waves, governance model and business case.
Build & migrate
Wave-based migration with automated validation and parallel runs.
Optimize
Cost, performance, quality monitoring and new data products.
Typical starting engagement
Data Platform Modernization Assessment
Fixed scope, fixed fee, and a clear decision at the end. Final pricing is confirmed after a short scoping call.
Scope this engagement →Starting at
- Estate and workload inventory
- Target architecture and platform recommendation
- Migration wave plan and timeline
- Total cost of ownership comparison
- Business case and risk register
FAQ
Frequently asked questions
Straight answers to the questions CIOs, CTOs and procurement ask us first.
Snowflake, Databricks or Fabric: which should we choose?
It depends on your workloads, skills, existing cloud and AI ambitions. Many enterprises use more than one. We run a structured evaluation against your use cases and total cost of ownership rather than defaulting to a favorite.
How long does a data platform migration take?
Typically three to nine months for a mid-sized estate, delivered in waves so value lands early. Large, complex estates can take longer.
How do you ensure data accuracy during migration?
Automated reconciliation at row, aggregate and business-metric level, plus parallel runs with sign-off by data owners before each cutover.
Can you make unstructured data usable for AI?
Yes: document processing, metadata enrichment, vector indexing and permission-aware access so documents can safely power RAG and agents.
Start here
Get a senior architect's view of your fastest path to value.
A 45-minute working session, not a sales pitch. Bring your toughest AI, data or cloud problem; leave with a point of view, a rough plan and a realistic budget range.
- Senior architects onlyNo account managers in the first meeting.
- Fixed-fee optionsClear scope, price and outcomes up front.
- Mutual NDA on requestBefore any detailed discussion.