30 days to data-driven: a governed analytics platform with Snowflake, AWS, GitLab CI/CD, dbt, Fivetran, Census, Metabase and Power BI

Business context and problem

Each team had “its own truth”, ad-hoc analytics and Excel reports. There was no reliable data, because governance and version-controlled releases were missing. Power BI pipelines were not reusable.

At the start of this engagement, the client’s analytics were slow, siloed, and unable to keep up with business demands. Critical metrics lived in disparate spreadsheets and databases, which made it hard for their leaders to get timely answers. Simple data requests turned into week-long back-and-forths, and different teams often reported different numbers for the “same” metric, a sure sign of inconsistent definitions and lack of governance. The time from question to insight was far too long, which frustrated stakeholders and caused missed opportunities. The client was also facing rising costs from maintaining legacy infrastructure that couldn’t scale or deliver the performance the business needed. In short, their data was underused, their analysts were overwhelmed, and trust in analytics was fading.

The core pain points were inconsistent metrics and slow delivery.

Approach overview

We proposed a bold plan. In 30 days, we would stand up a modern analytics stack that would address these pain points and position the client for strategic agility. Rather than a lengthy traditional IT project, we used cloud-based, best-of-breed tools. This modular stack promised quick setup and iteration, without heavy upfront costs or lengthy deployments. As well as delivering quickly, we set out to cut costs, accelerate time to insight, improve system agility, and enforce data governance from day one.

The stack balances speed, governance, and cost:

• Snowflake for elastic compute and storage, with environment isolation through zero-copy cloning (instant, metadata-only copies). It is a single source of truth with virtually unlimited scalability and usage-based pricing, so the client “only pays for what they use”.
• Dev, QA and prod environments via Snowflake zero-copy clones for safe iteration.
• Amazon S3 as a landing zone and data lake via a Snowflake storage integration (an IAM role with an external ID, no embedded keys).
• Private VPC with a bastion and an S3 gateway endpoint; no inbound SSH or public S3 egress from runners.
• GitLab CI/CD on EC2 in a private VPC behind a bastion (no inbound SSH).
• dbt Core for version-controlled transformations and tests in pipelines. It brings software engineering rigour (version control, automated testing, CI/CD) to the client’s data transformations and catches issues before they hit production.
• Fivetran to ingest SaaS and database sources with managed ELT.
• Census for reverse ETL back to CRM and operational tools.
• Power BI as the governed semantic model consumed via live connections.
• Metabase to give non-technical users self-serve analytics, which ends the bottleneck where every report request becomes a support ticket. Letting their staff explore data and build visualisations frees the client’s analysts to focus on high-value analysis instead of ad-hoc queries.
• FinOps through Snowflake budgets (alerts and webhooks), resource monitors, and account usage cost dashboards.

High-level architecture

Data lands in S3 or via connectors, and transformations run in Snowflake (dbt). CI guards quality before deployment, governed models feed the BI layer, and activation pushes “last mile” data to operational tools.

Step-by-step execution

We broke the 30-day timeline into phases. In week 1, we spun up Snowflake and started ingesting the client’s key data sources. By week 2, we had a functional dbt project modelling their core datasets with tests in place. In week 3, we deployed the BI layer with the first dashboards for their business users. In the final week, we fine-tuned performance (for example, warehouse sizing and query optimisations) and formalised governance. The result was a production-grade, cloud-based analytics platform in a month: infrastructure, tooling and first analytics, end to end.

1. Snowflake foundations and environments

   • Dev, QA and prod via zero-copy cloning, for instant environment creation and rollback.
   • Clones are metadata-only and don’t duplicate data files, so testing is safe and fast.
   • We tuned warehouse sizing, caching and clustering to cut the client’s Snowflake costs by 50% without sacrificing performance, while still delivering sub-minute builds on multi-billion-row fact tables.

2. CI/CD with GitLab on EC2

   • Autoscaling GitLab Runner on EC2 (Docker executor) to spin up ephemeral build agents.
   • Pipeline stages: ELT triggers (call the Fivetran API to sync critical connectors); custom Python scripts and S3 ingestion; dbt build and tests (unit, unique, not_null, accepted_values, relationships, and so on); reverse ETL to CRM and operational tools; docs generation.

3. Transformations and data quality with dbt

   • Build staging, then intermediate, then marts.
   • Generic tests (unique, not_null, accepted_values, relationships).
   • Model builds depend on tests passing.
   • Publish dbt docs as an artefact for lineage.

4. Ingestion with Fivetran and reverse ETL with Census

   • Fivetran covers databases (for example, PostgreSQL), SaaS apps, and files with managed schemas and incremental syncs.
   • Census syncs curated marts back to CRM, marketing, CS, and finance systems, which solves the “last mile” of activation.

5. Power BI and Metabase as the consumption layer

   • Publish Power BI semantic models and let report authors connect.
   • Point their business users at the curated, dbt-derived marts in Metabase rather than raw tables, so they can explore a “Sales by Product” table safely without knowing how it was calculated.
   • Set permissions so each of their teams sees the data relevant to them, and executives get cross-domain views.

6. FinOps: cost controls and observability

   • Budgets and resource monitors set monthly credit limits for the account or for object groups, with alerts by email.
   • Usage dashboards give observability of core resources.
   • Snowsight cost management gives quick organisation-level views.

Results

• Faster time to insight. The platform we delivered gets the client to insight 80% faster than before. Reports that once took days of manual data wrangling are available on interactive dashboards in seconds. The time from a business question to a data-backed answer shrank, which sped up decision-making in all their departments.
• Cost optimisation. By moving the client to Snowflake and optimising its usage, we cut their data warehousing costs by 50%. The pay-as-you-go model, combined with the compute tuning we did, gets them better performance at half the cost of their previous setup. The saving came without any loss of service quality.
• System agility and scalability. The new stack scales with the client’s growing needs. It handles multi-billion-row workloads without performance issues, and adding a new data source or metric is no longer a major project for them. It’s a routine task. This agility has made their business more responsive; for example, when a new opportunity or KPI emerges, their data infrastructure can support it within days. The platform can grow with the business.
• Governance and trust. All transformations are now governed in a single place (dbt), and rigorous tests and CI/CD ensure that no bad data or broken logic makes it to production. Feature branches are promoted through dev, QA and prod with deterministic tests and clones, so releases no longer break dashboards. Since go-live the client has had zero major incidents or “number discrepancies” in executive reports. Consistent definitions have eliminated metric confusion across their teams.
• Analyst productivity. Freeing the client’s analysts from routine data pulls and one-off report requests reclaimed significant productive time. The estimate is at least 15-20 hours per analyst per week freed from manual reporting, time which is now spent on deeper analysis and strategic projects. With self-serve tools handling most ad-hoc queries, their two-person data team supports a much larger organisation, in their case 40+ active data users.
• Faster development cycles. The iterative development and testing we introduced have shortened the client’s development cycle for analytics improvements. New data models or dashboard features that used to take weeks of careful manual testing and deployment now roll out in a day or two, thanks to automated testing and environment isolation. This means they can respond to business needs on the fly.
• Private-by-default network. There is no inbound SSH, S3 access stays inside the VPC, and there are fewer egress paths to audit.
• Predictable costs. Proactive alerts fire at 60%, 80% and 100% of monthly budgets. Warehouses auto-suspend. BI dashboards show top resources.
• Business impact. One semantic model powers many Power BI reports. Operational teams act on trusted metrics and receive synced segments in the tools they already use.

For the client, these results mean lower operating costs, faster and better-informed decisions, and a more data-savvy workforce. The modern stack paid for itself within their first quarter of use, through cost savings and efficiency gains, and continues to compound value as they scale.