Modern analytics stacks,
built right

We design, build, and modernize analytics stacks that separate reporting from your backend - so your data is reliable, scalable, and easier to evolve. Delivered faster through AI-assisted engineering.

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Common Failure Modes

When reporting is tied to your backend, everything slows down.

Backend-coupled reporting, ad hoc scripts, brittle pipelines, and unclear ownership all create the same problem: analytics stays hard to trust and slow to change. The fix is a clean analytics stack with reliable models, clear layers, and better operating discipline.

BACKEND + SOURCES Product Apps cloud_sync Backend DBs database SaaS Tools bolt Ad hoc Feeds terminal CLEAN ANALYTICS FLOW Ingestion / ELT Warehouse Transformation Layer Orchestration AI OVERLAY Delivery Acceleration Iteration Support Model Drafting Documentation QA Suggestions OUTPUTS / REPORTING Reports monitoring Semantic Layer api Docs menu_book Dashboards layers
What Platinur Does

Three ways to engage.

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Architecture Review

Review the current stack, identify structural risks, and define the right target architecture, migration path, and implementation roadmap.

Book the review
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Modern Analytics Stack Build

Design and implement the warehouse, transformation layer, orchestration, reporting models, and operating standards needed for reliable analytics at scale.

Warehouse / Models / Scheduling / Reporting

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Modernization & Migration

Move from backend-coupled reporting, legacy ETL, and brittle scripts to a cleaner ELT architecture with proper transformation, ownership, and reporting layers.

Backend separation / ELT / Refactoring

AI-assisted delivery, applied where it actually helps

Model drafts, documentation, QA, and iteration - while the output remains a grounded analytics stack.

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Selected Capabilities

Architecture-led patterns, with tools chosen to fit the stack.

Ingestion & ELT

Airbyte

Warehousing

BigQuery, Snowflake

Transformation

dbt

Orchestration

Dagster

Reporting / Semantic Layer

Looker

Example engagement

Architecture review to phased implementation

"Reporting is too tied to our backend and changes are getting harder every quarter."

Current state

  • Product or application tables are powering reporting
  • Logic is scattered across ad hoc SQL and scripts
  • Changes are risky and slow
  • Ownership is unclear

Recommended path

  • Introduce a warehouse layer
  • Add a dbt-style transformation layer
  • Migrate brittle reporting logic in phases
  • Establish documentation and ownership as the stack stabilizes
How We Work

Senior architecture and implementation, end to end.

Platinur diagnoses what is brittle, designs the target stack, implements in controlled phases, stabilizes the foundation, and leaves the team with a system that is easier to operate and extend.

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    1. Assess first

    Understand current architecture, reporting needs, ownership, and failure points.

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    2. Design the target stack

    Define the right layers, operating model, and migration path before implementation begins.

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    3. Implement in phases

    Move toward the target architecture in controlled steps that reduce delivery risk.

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    4. Stabilize the foundation

    Improve reliability, testing, documentation, and maintainability.

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    5. Enable the team

    Leave behind a system that is easier to operate, extend, and evolve.

AI is used where it improves speed and quality.

Especially in model drafting, documentation, QA, and iteration - while the primary deliverable remains a clean analytics stack.

Start a modernization conversation