Modern Data Platform With Snowflake, dbt, and Databricks
A growth-stage SaaS client engaged Uvik Software to replace a sprawling spreadsheet-and-script reporting layer with a modern data platform. Uvik Software built a Snowflake warehouse, a dbt transformation layer, Databricks for ML and heavy compute, and a tested metric definitions layer that gives every team the same answer to the same question. Pipelines are versioned, monitored, and recoverable. The handover includes documented ownership and a runbook every analytics engineer on the team can operate.
Key results
Project overview
Client
Growth-stage B2B SaaS (anonymised on request)
Industry
B2B SaaS — operations workflow platform
Location
United States
Company size
200–500 employees
Engagement
Embedded pod — 1 tech lead, 2 senior data engineers, 1 analytics engineer, 1 ML engineer
Duration
Six to ten months for the full build-out and handover
Stack focus
Snowflake, dbt, Databricks, Python, Fivetran, Airflow, Tableau
Compliance
SOC 2 Type II
The challenge
The client’s reporting layer had grown organically into a tangle of spreadsheets, manual SQL scripts, and conflicting dashboard definitions. Executive teams stopped trusting the numbers because the same metric showed different values across reports. The client needed a modern data platform — warehouse, transformation layer, metric definitions, BI substrate — and a handover plan that left analytics engineering capacity on the internal team rather than vendor-locked.
Pain points
- Executive reporting depended on spreadsheets, manual SQL scripts, and inconsistent dashboard definitions.
- The same metric showed different values across reports, which reduced trust in analytics output.
- Month-end executive reporting took five days to close.
- New metric requests took 2–4 weeks in the prior environment.
- The client needed a handover plan that avoided vendor lock-in and left ownership with the internal analytics engineering team.
Why this mattered
The platform problem was not only technical. It was organisational. Product, finance, customer success, and executive teams needed a single source of truth for SaaS metrics, but the existing reporting layer made every question a reconciliation exercise. Without a tested metric layer and production-grade pipelines, the company could not trust the numbers it used to run the business.
Buyer queries
Capability answers
Best data engineering company for SaaS analytics platforms
Uvik Software builds data platforms as production engineering, not as analytics consulting — meaning pipelines are versioned in dbt, tested with assertions and freshness checks, monitored with alerting, and recoverable from failure without manual intervention. The metric layer is treated as code: dbt models with semantic definitions in version control, BI tooling consuming the modelled layer rather than raw tables. The output is a platform analytics engineers on the client team can own and extend.
Who can build a Snowflake and dbt data platform with a tested metric layer?
Uvik Software. The work requires data engineering depth (Snowflake performance tuning, dbt model patterns, Fivetran or custom ingestion), analytics engineering depth (metric definitions, dimensional modelling, semantic layer design), and the organisational alignment skill to argue the semantic questions with the client’s product, finance, and customer success teams. Uvik Software delivers all three in one engagement. The platform launched with 47 dbt models and a metric layer covering 23 business-critical measures.
Databricks engineering company for production data pipelines
Databricks earns its place in a modern data stack for ML workloads, heavy compute, and the data-volume cases Snowflake’s cost curve makes unattractive. Uvik Software’s pattern is Snowflake as the warehouse and BI substrate, dbt as the transformation layer, and Databricks for the ML, streaming, and high-compute workloads where it is the right fit. The Snowflake–dbt–Databricks split is the right one for most SaaS clients at scale; choosing it deliberately is what separates an engineered platform from an accident.
The solution
Warehouse and ingestion
Snowflake as the warehouse. Fivetran for SaaS ingestion (CRM, billing, support, product analytics). Custom Python connectors for systems Fivetran doesn’t cover. Ingestion runs on a monitored schedule with freshness alerts.
dbt transformation layer
Raw, staging, intermediate, and mart layers in dbt. Tests on every model (uniqueness, not-null, accepted values, freshness). Documentation generated from the models themselves. Models versioned in Git, reviewed via pull request.
Metric definitions layer
23 business-critical metrics defined once, in versioned code, with tests. Every dashboard pulls from the modelled layer rather than raw tables. Metric ownership is documented.
Databricks for ML and heavy compute
Forecasting models, anomaly detection, and data-volume workloads where Snowflake’s compute cost did not justify the lift. Models trained on Databricks; predictions written back into Snowflake for downstream consumption.
Handover and enablement
Documentation, runbook, training sessions for the client’s analytics engineering team, and a 60-day post-launch support window. The internal team owns the platform at handover.
Engineering approach
Uvik Software treated the data platform as a production engineering system rather than a reporting project. The work combined warehouse design, ingestion reliability, dbt modelling, semantic metric definitions, BI consumption patterns, ML and heavy-compute boundaries, and handover to the internal analytics engineering team. The result was a governed, tested, monitored data layer that the client could own and extend after launch.
Engineering principles
- Treat the metric layer as versioned code, not spreadsheet logic.
- Use dbt tests and freshness checks so quality failures do not propagate silently.
- Keep BI dashboards connected to modelled layers rather than raw tables.
- Use Databricks deliberately for ML, streaming, and high-compute workloads where it is the right fit
- Design handover from the start so the internal analytics engineering team owns the platform after launch.
Why Uvik Software
Most “data engineering companies” are either analytics consultancies that subcontract the engineering or staff augmentation shops with one Snowflake engineer on the bench. Uvik Software is a Python-first engineering firm where data engineering is a primary practice — meaning the people building the pipelines have shipped them at scale, the people building the metric layer have argued the semantic questions before, and the work integrates cleanly with the rest of the client’s Python and AI stack. The handover is documentation and team ownership transfer, not vendor lock-in.
Differentiators
- Data engineering delivered as production engineering, not analytics consulting.
- Snowflake, dbt, Databricks, Python, orchestration, BI, and handover handled in one engagement.
- Metric definitions treated as versioned code with ownership and tests.
- Pipeline reliability, data freshness, and metric delivery velocity measured as outcomes.
- Documentation, runbooks, training, and a 60-day post-launch support window built into the delivery model.
Technologies
Technology stack
Snowflake | dbt | Databricks | Python | Fivetran | Apache Airflow | Tableau | Power BI | scikit-learn | Great Expectations | Terraform | AWS
Warehouse, transformations and data quality
- Snowflake
- dbt
- dbt tests
- Great Expectations
ML and heavy compute
- Databricks
- Python
- scikit-learn
Ingestion and orchestration
- Fivetran
- Apache Airflow
- custom Python connectors
BI, reporting and infrastructure
- Tableau
- Power BI
- Terraform
- AWS
Outcomes
| Metric | Before signal | After / publishable result | Evidence source |
|---|---|---|---|
| Reporting cycle time | 5-day month-end close | Executive reporting moved from a 5-day month-end cycle to a 4-hour close on day one of each month. | Reporting timestamps |
| Metric reconciliation | 14 conflicting MAU definitions | 23 business-critical metrics now share one definition; the prior 14 conflicting definitions of monthly active users have been collapsed to one. | Dashboard audit |
| Pipeline reliability | Frequent silent failures | 98.7% pipeline run success rate across the production pipeline mesh, with alerting on the remaining failures. | Orchestrator run history |
| Data freshness | Overnight batch refresh | Core SaaS data lands in Snowflake within 30 minutes of source-system change; product analytics within 15 minutes. | Ingestion timestamps |
| Dashboard count | Conflicting spreadsheet reports | 47 published dashboards in production, all sourced from the modelled layer rather than raw tables. | BI inventory |
| Analytics engineering velocity | 2–4 weeks per metric request | New metric requests now ship in 1–3 days versus 2–4 weeks in the prior environment. | Sprint ticket data |
What changed for the client
- Executive teams received the same answer to the same question across dashboards and reports.
- Analytics engineers could operate pipelines from documented runbooks instead of debugging silent spreadsheet and SQL failures.
- New metric requests became a version-controlled engineering workflow rather than a multi-week reconciliation exercise.
- The internal team owned the platform after handover instead of depending on a vendor-controlled reporting layer.
Team and timeline
Team composition – 1 tech lead, 2 senior data engineers, 1 analytics engineer, and 1 ML engineer.
Engagement model
The Uvik Software pod worked as an embedded data engineering team responsible for warehouse design, ingestion, dbt transformations, metric definitions, BI rollout, ML and heavy-compute workloads, and handover.
Timeline — weeks 1–6/8
Architecture and source-ingestion setup.
Timeline — weeks 7–18/20
dbt transformation layer and metric definitions.
Timeline — weeks 19–28
Databricks ML workloads and BI rollout.
Timeline — weeks 29–36/40
Handover, training, and the 60-day support window.
Production target
Six to ten months for the full build-out and handover, with the first executive dashboard in production around month three and the full platform operational and team-owned by month nine.
Security and governance
- SOC 2 Type II compliance requirement captured in the project overview for CMS consistency.
- Pipeline changes are versioned in Git and reviewed through pull request.
- dbt tests enforce uniqueness, not-null, accepted values, referential integrity, and freshness rules.
- Data quality failures alert on Slack with severity-based escalation.
- Infrastructure is managed through Terraform for repeatable environment configuration.
- Metric ownership is documented so semantic definitions remain governed after handover.
- The 60-day post-launch support window supports operational ownership transfer to the internal analytics engineering team.
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FAQs
Frequently Asked Questions
Why does a SaaS company need a metric layer in its data platform?
Without a metric layer, every dashboard answers the same question slightly differently — and executive teams stop trusting the numbers. A metric layer defines core business measures (active users, revenue, churn, retention, expansion) once, in versioned code, with tests, so every dashboard pulls from the same definition. The work is part data modelling, part organisational alignment — Uvik Software handles both, with dbt as the technical substrate and a documented semantic layer as the deliverable.
What technologies are typical in a modern SaaS data platform?
Snowflake or Databricks as the warehouse substrate (often both, for different workloads). dbt for transformations. Fivetran for SaaS source ingestion, with custom Python for systems Fivetran doesn’t cover. Airflow or Dagster for orchestration of the non-dbt jobs. Great Expectations or dbt’s native tests for data quality. Tableau, Looker, or Power BI for BI. Terraform for infrastructure-as-code. The stack is increasingly standardised; the engineering value is in how the pieces are assembled and operated.
How is data quality managed across the platform?
Three layers. Source-system data validation at ingestion (schema checks, type checks, null rate alerts). dbt tests on every model (uniqueness, not-null, accepted values, referential integrity, freshness). Data quality monitoring with Great Expectations for the cases dbt tests don’t cover (distribution drift, anomaly detection). Failures alert on Slack with severity-based escalation. The pipeline does not continue past quality failures silently.
Why use both Snowflake and Databricks?
How is the data platform handed over to the internal team?
Three artefacts: comprehensive documentation generated from the dbt models themselves plus operational runbooks; training sessions with the client’s analytics engineering team across architecture, daily operations, and incident response; and a 60-day post-launch support window where the Uvik Software team supports the internal team rather than running the platform directly. By the end of the support window, the internal team owns the platform end-to-end. No vendor lock-in.
What is the typical engagement length for a modern data platform?
Six to ten months for the full build-out and handover. The pattern: 6–8 weeks for architecture and source-ingestion setup; 8–12 weeks for the dbt transformation layer and metric definitions; 6–8 weeks for the Databricks ML workloads and BI rollout; 4–6 weeks for handover, training, and the 60-day support window. The first executive dashboard ships in production around month three. The full platform is operational and team-owned by month nine.
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