Data Architecture & Platform Design
The right infrastructure for your stage and your ambitions.
Data infrastructure at Series A and B companies is typically inherited rather than designed. A warehouse selected under time pressure, transformation logic that has never been documented, reporting that lags two weeks behind the decisions it is meant to inform. The downstream consequence is a data function that cannot move at the speed the business requires.
We scope engagements to a defined deliverable: warehouse selection and implementation, a zero-downtime platform migration, or a complete data stack design from ingestion to serving layer. Every engagement concludes with full documentation and a structured knowledge transfer. Your team owns what was built.
We do not operate on open-ended retainers. Every engagement has a clear definition of done.
What we deliver
Select a service area to see how we approach it.
Choose and build the right foundation for your data at any scale.
- 01
Audit your existing workloads, query patterns, team skills, and cost constraints.
- 02
Model projected costs and performance across candidate platforms against your actual usage.
- 03
Produce a recommendation document with rationale, trade-offs ruled out, and risk factors.
- 04
Implement the chosen warehouse: schema design, access controls, ingestion configuration.
- 05
Validate with your team and hand over full architecture documentation and runbooks.
Move platforms without disrupting the teams that depend on your data.
- 01
Inventory every downstream consumer: dashboards, pipelines, embedded queries, and APIs.
- 02
Design the cutover strategy and rollback procedure before writing a line of migration code.
- 03
Begin parallel dual-write: new platform receives the same data as the existing one.
- 04
Validate parity across the full business cycle — row counts, aggregates, edge cases.
- 05
Migrate consumers one by one, with a practiced rollback ready at each step.
- 06
Execute the final cutover during a low-traffic window with the rollback procedure staged.
Assemble best-in-class tools that fit your team's workflow and budget.
- 01
Map your data sources, current tools, team skills, and 12-month roadmap requirements.
- 02
Evaluate options at each of the four layers: ingestion, transformation, orchestration, serving.
- 03
Model total cost of ownership — licensing plus engineering time to operate each tool.
- 04
Design the integration architecture so each layer hands off cleanly to the next.
- 05
Implement, configure, and document each layer with runbooks your team can maintain.
Structure your data so every team can trust and use it consistently.
- 01
Align on the business questions that the data model needs to answer — before touching a table.
- 02
Inventory raw source data: understand grain, relationships, and reliability of each source.
- 03
Design the dimensional model: fact tables, dimensions, and layer separation in dbt.
- 04
Build and test in dbt with schema tests, freshness checks, and documentation.
- 05
Validate outputs with stakeholders and write Architecture Decision Records for every major choice.
A modern, maintainable data stack with complete documentation. A team that's confident running what was built. Architecture decisions you won't need to revisit for years.
CTOs and Heads of Engineering at Series A/B companies on legacy infrastructure, planning rapid scale, or evaluating a platform change.
We publish in-depth playbooks on data engineering best practices at handbook.bottomlinedata.co. Detailed guides related to this practice area will be linked here.
Start a conversation.
Every engagement begins with a focused discussion of your current data environment and priorities. To schedule an initial consultation, reach out directly.
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