Data engineering
The data foundation your AI will depend on
Pipelines, warehouses and lakehouses, vector and graph stores, built with contracts, quality gates, lineage and access control from the first commit, and built to be handed over.
Technology we use for this work
The problem
The model is rarely the blocker
Fragmented sources, undocumented joins, no lineage and no owner. Every model built on top inherits the ambiguity, and every answer becomes a debate about which number is right.
We build the layer that decides whether anything above it can be trusted, and we build it first. Quality gates and lineage land before any model does.
What we build
What does our data engineering service include?
Pipelines, lakehouses, vector and graph stores with contracts, quality gates, lineage and access control.
Ingestion and integration
Batch and streaming pipelines across databases, SaaS APIs, files, event buses and third-party data providers, with replay and backfill built in.
Warehouse and lakehouse architecture
Dimensional and lakehouse models on BigQuery, Snowflake, Redshift, Databricks, Postgres and DuckDB, sized to the question rather than to the vendor.
Vector and graph storage
Embedding stores and property graphs run alongside the warehouse, so semantic search and relationship reasoning share one governed source.
Semantic layer
One definition of revenue and one definition of an active customer, versioned in code and consumed identically by dashboards and agents.
Data quality as a build gate
Schema contracts, deterministic profiling and validation rules that fail the pipeline rather than the quarterly board pack.
Governance and access control
Row-level security enforced in the database, least-privilege service roles, PII detection and redaction, retention policy and audit trails.
Migration and modernization
Moving off spreadsheets, legacy SSIS and hand-run scripts onto reproducible, resumable jobs with an explicit exit-code contract.
Cost and performance
Partitioning, clustering, incremental models and query cost budgets, reviewed during delivery rather than after the first invoice.
Architecture
How does it work, step by step?
-
Sources
Operational systems
- Databases and SaaS APIs
- Files and spreadsheets
- Events and third-party feeds
-
Ingest
Pipelines
- Batch and streaming
- Replay and backfill
- Schema contracts
-
Store
Governed platform
- Warehouse or lakehouse
- Vector and graph stores
- Row-level security
-
Model
Semantic layer
- Versioned metric definitions
- Quality gates
- Lineage
-
Serve
Consumers
- Dashboards
- ML models and agents
- Embedded analytics
Proof
Where have we delivered this?
Conversational BI for teams with no data team
Connect a spreadsheet or database and come back to a dashboard that already means something, then reshape it by asking in plain language.
Result: 100% of model calls routed through one traced gateway
Read the case studyGoverned BI and a financial data platform for banking and insurance
Semantic layers, a governed financial warehouse and BI across five business units, with strict access control and automated report distribution.
Result: 50% faster dashboards on insurance BI
Read the case studyBook recommendations at scale, with a self-hosted LLM
A hybrid recommendation engine over more than 10 million titles and a goal-based recommendation chatbot on a self-hosted Llama 3 model.
Result: 60% higher click-through on recommendations
Read the case studyContext
Industries and related services
Industries
Related services
FAQ
Data engineering: common questions
Do we need a data platform before we can use AI?
You need enough of one. The assessment identifies the minimum foundation for the first use case: the sources, contracts, access model and quality checks that use case depends on. We do not build a platform for its own sake.
Warehouse or lakehouse?
It depends on data types, volume, existing tools and team skills. Structured reporting on moderate volumes is usually well served by a warehouse. Mixed structured and unstructured data at scale often suits a lakehouse. We recommend one with the reasoning in writing.
Can you work with our existing stack?
Yes. Where a client already has a platform, we build on it rather than around it. We work across BigQuery, Snowflake, Redshift, Databricks, Postgres, SQL Server and MongoDB.
How do you handle sensitive data such as PHI or PII?
Sensitive fields are identified in the first phase. We apply detection and redaction, least-privilege roles, row-level security in the database, retention rules and audit trails, and treat data residency as an architecture constraint.
How do you make pipelines reliable?
Every job is resumable at stage level, commits per record rather than once at the end, has an explicit exit-code contract and writes failures as structured data. A run report is produced on every outcome, so nobody has to guess whether a run finished.
Start with a two to three week assessment.
Send us the problem and the constraints. You get a written architecture, a risk register and a costed roadmap, and you keep them whatever you decide next.