PLATFORM
Data your decisions can stand on.
The foundation everything else depends on. Pipelines, models, and warehouses built so every downstream decision can be trusted, with complete data delivered on time, every time.
A steady, trustworthy pipeline is something the rest of the business can build on without a second thought. That is what a data platform is for: the same numbers, in the same place, whether it is a Tuesday standup or a board deck.
SIGNAL IN THE NOISE
The dashboard and the spreadsheet never agree.
- Finance closes the month with one number. Product reports a different one from the same underlying event. Nobody can say which pipeline is wrong without a day of Slack archaeology.
- A schema change three teams upstream silently breaks a report two teams downstream, and the first anyone hears of it is a client asking why their numbers moved.
- The warehouse bill keeps climbing faster than the data volume that justifies it, because every team built its own transformation logic instead of sharing one.
- One person understands how the nightly jobs actually depend on each other. Their vacation is a business continuity risk.
THE PULSE, NAMED
Six ways the pulse stays steady.
Ingestion & Change Data Capture
Data arrives from wherever it actually lives, such as application databases, SaaS APIs, event streams, and third-party files, captured as change rather than re-extracted in bulk on a schedule that always lags reality. CDC keeps a warehouse current without hammering the source system it depends on.
Streaming & Batch Processing
Not every number needs to be real time, and not every real-time promise is worth its cost. We decide streaming versus batch per use case: fraud signals and operational alerts stream, monthly cohort analysis batches, and we make that decision explicit instead of defaulting to whichever is fashionable.
Lakehouse & Warehouse Modelling
A warehouse without a modelling discipline is just an expensive place to store confusion. We design dimensional models or a lakehouse table layout that matches how the business actually asks questions, so a new analyst can find the right table without asking a data engineer first.
Transformation, Contracts & Quality Testing
Transformations live in version control, reviewed like application code, with tests that fail the build before they fail a board report. Data contracts between producing and consuming teams turn 'nobody told us the schema changed' into a build-time error instead of a Friday afternoon incident.
Orchestration & Lineage
Every job's dependencies are declared, not assumed. When something breaks, lineage tells you in seconds which downstream tables and dashboards are now stale, well before a customer notices.
Catalog, Governance & Cost
A searchable catalog with clear ownership, column-level access control for sensitive fields, and cost visibility per pipeline so 'why did the warehouse bill double' has an answer that does not require a forensic audit.
THE TIMELINE
Four phases, one pulse.
- 2–3 weeks
Discovery & Data Audit
- Data Audit Report
- Source-to-report dependency map
- 4–6 weeks
Reference Pipeline & Contracts
- Working reference pipeline for one priority domain
- First data contracts between producing and consuming teams
- Modelling standards document
- 8–14 weeks
Platform Build-Out
- Production pipelines per domain
- Catalog with ownership assigned
- Quality test suite
- 3–4 weeks
Hardening, Documentation & Handover
Handover Pack: runbooks, cost dashboard, recorded training
TECHNOLOGY
The stack, named.
- Ingestion/CDC
- Fivetran or Airbyte for managed connectors; Debezium for database change data capture at the source.
- Streaming
- Kafka or Redpanda as the log; Apache Flink for stream processing where latency actually matters.
- Orchestration
- Airflow (3.x) or Dagster; Prefect where a lighter operational footprint is preferred.
- Transformation
- dbt (Cloud or Core) as the default; SQLMesh where virtual environments and column-level lineage are a priority from day one.
- Warehouse/lakehouse
- Snowflake or BigQuery for warehouse-first teams; Databricks with Delta Lake, or Apache Iceberg on object storage, for lakehouse-first teams already investing in open table formats.
- Quality & contracts
- Great Expectations or Soda for data quality tests; dbt tests plus schema-level contracts enforced in CI for producer/consumer agreements.
- Catalog & lineage
- DataHub or OpenMetadata (self-hosted); Atlan where a managed catalog with less operational overhead is preferred.
- Governance
- Column-level access control and masking via the warehouse's native governance layer (Snowflake Horizon, BigQuery column-level security, or Databricks Unity Catalog).
GOVERNANCE
Trust, made checkable.
- Security: Column-level access control and masking for PII/financial fields, applied at the warehouse layer so it holds regardless of which BI tool queries it.
- Compliance: Lineage and catalog ownership records answer "where did this number come from and who is accountable for it," the exact question regulators and auditors ask.
- Cost control: Per-pipeline and per-team cost attribution inside the warehouse's native cost tools, reviewed monthly against a budget, not discovered at invoice time.
- Auditability: Every transformation is version-controlled and code-reviewed; every schema change goes through a contract check before it can break a downstream consumer.
- Operational ownership: A named on-call rotation, a runbook per domain pipeline, and a 90-day post-handover support window before full transition to your team.
QUESTIONS
What people ask before they call.
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