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Data Engineering & Modern Data Platforms

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Everything downstream depends on this layer. We design and build the pipelines, warehouses, lakehouses and semantic models that analytics, BI, activation and AI agents all read from — on Snowflake, Databricks, BigQuery or your existing platform. Modelled with dbt, tested in CI, documented in a catalogue, and governed so that one metric means one thing everywhere.

Challenges we solve

Data lands in five places. Marketing extracts it into a spreadsheet, finance into a different one, product builds a Looker model, and someone in growth has a Python notebook that only they can run. Each is defensible on its own and none of them agree. Adding a BI tool or an AI agent on top of that does not fix it; it multiplies it. The fix is a modelled, tested, documented layer between raw data and everything that consumes it — with metric definitions that live in code, tests that fail the build when they break, and lineage so you can answer "where did this number come from" in under a minute.

What we deliver

Data platform architecture

Warehouse and lakehouse design on Snowflake, Databricks, BigQuery, Redshift or Synapse: storage and compute sizing, environment separation, medallion or layered modelling, workload isolation, cost controls and a migration path from whatever you have now.

Ingestion and pipeline engineering

Batch and incremental ingestion from analytics platforms, ad platforms, CRM, ERP, product databases, files and third-party APIs — built with Fivetran, Airbyte, native connectors or custom extractors where a connector does not exist, and orchestrated in Airflow, Dagster, Databricks Workflows or Cloud Composer.

Streaming and real-time data

Event streaming with Pub/Sub, Kafka, Kinesis or Event Hubs, plus stream processing for near-real-time use cases such as personalisation signals, fraud checks, operational alerting and live inventory — with a clear view of where real time genuinely earns its cost.

Transformation and modelling with dbt

Layered dbt projects with staging, intermediate and mart models, incremental strategies, snapshots for slowly changing dimensions, macros for reuse, and generated documentation. Tests and freshness checks run in CI so broken models never reach production.

Conversions API and Enhanced Conversions

Meta CAPI, Google Enhanced Conversions for web and leads, LinkedIn CAPI, TikTok Events API and Pinterest API for Conversions — deduplicated against browser events, hashed correctly, and match-rate monitored after launch rather than assumed. This is usually where lost conversion signal is recovered.

Semantic layer and metric definitions

A governed semantic layer — dbt Semantic Layer, Looker, Cube or platform-native — where metrics, dimensions, joins and verified queries are defined once. This is what makes conversational BI and AI agents trustworthy rather than plausible.

Mobile and app measurement

Firebase and GA4 for Android and iOS, SDK implementation and review, app-to-web journey stitching, deep-link and campaign attribution, SKAdNetwork and Privacy Manifest considerations, and consistent event naming between app and web.

Reverse ETL and activation

Pushing modelled segments, scores and attributes back into the tools that act on them: CRM, ad platforms, email and CDP, using Census, Hightouch or native APIs, with sync monitoring and identity mapping.

Data quality, testing and observability

Schema, volume, freshness, uniqueness and referential tests, anomaly detection on key metrics, alerting into Slack or your incident tooling, and a data incident process with named owners.

Cataloguing, lineage and cost management

Column-level lineage, a searchable catalogue, ownership metadata, and continuous warehouse cost management — query optimisation, clustering and partitioning, warehouse sizing, and spend alerts before the invoice rather than after.

Platforms & Tooling

  • Snowflake
  • Databricks
  • BigQuery
  • Redshift
  • Azure Synapse
  • dbt
  • Airflow
  • Dagster
  • Fivetran
  • Airbyte
  • Kafka
  • Pub/Sub
  • Census
  • Hightouch

Process

Week 1–2

Assess

Source inventory, current pipelines, cost and quality baseline.

Week 3–4

Design

Target architecture, modelling approach, governance and SLAs.

Week 5–12

Build

Ingestion, transformation, semantic layer and tests in sprints.

Week 13–16

Migrate

Parallel run, reconciliation, cutover and decommissioning.

Ongoing

Operate

Monitoring, cost optimisation and a rolling model backlog.

FAQs

It depends on workload shape, existing cloud commitments and team skills rather than on which is objectively best. Databricks tends to win where heavy ML and unstructured data dominate; Snowflake where SQL analytics and data sharing dominate; BigQuery where you are already deep in GCP and GA4. We are not resellers for any of them, and the architecture review makes the trade-offs explicit against your workloads.

dbt gives you modelled tables. A semantic layer gives you governed metrics on top of them, which is what BI tools and AI agents need in order to answer a question the same way twice. If conversational BI or agents are anywhere on your roadmap, the semantic layer stops being optional.

Yes, and that is usually the right call. Migrations are justified by cost, capability or consolidation — not by preference. Most of our engagements improve modelling, testing and governance on the platform you already have.

Incremental models instead of full refreshes, sensible clustering and partitioning, right-sized compute with auto-suspend, query review for the expensive tail, and spend monitoring with alerts. We baseline cost at the start so the change is measurable.

The first modelled dataset and a working dashboard on it typically land in weeks three to five. We deliberately sequence a visible early slice rather than building the full platform before anyone sees value.

Your team. Everything is in version control, tested, documented and handed over with enablement sessions. If you would rather we kept running it, that is an embedded pod arrangement rather than a dependency we engineer in.

Your team. Everything is in version control, tested, documented and handed over with enablement sessions. If you would rather we kept running it, that is an embedded pod arrangement rather than a dependency we engineer in.

Ready To Make Your Data Work Harder?

Let’s build a trusted measurement foundation that drives smarter decisions and measurable growth.

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