Design & Build Modern Data Platforms in Days.
Building a new data platform from scratch takes months of architecture decisions, data modeling, and pipeline development before a single table goes live. 3X Forward Engineer transforms API specs, CSVs, JSON feeds, Excel schemas, and DDLs into fully architected Snowflake, Databricks, Fabric, or BigQuery platforms with production-ready schemas, data models, ETL pipelines, and deployable code, delivered in days.
Source data specs and requirements shouldn't take months to become a platform.
Every greenfield data platform project stalls on the same bottlenecks: months of manual architecture design, unclear paths from source data to analytical models, and a shortage of senior architects to make the right decisions. 3X Forward Engineer eliminates them at scale.
Months of Manual Architecture Design
Greenfield data warehouse, lakehouse, and analytical platform builds consuming months of senior architect time for schema design, layer definitions, and pipeline architecture. Critical projects queue up waiting for architecture decisions while teams with source data in hand have no clear path to a production-ready platform.
Source Data Without a Clear Path
Teams starting with API specs, JSON feeds, CSVs, Excel files, and business KPIs but no structured methodology to translate raw source data into a fully architected analytical platform. Engineers know what data they have and what outcomes they need, but the gap between source inputs and a deployable data platform takes months to bridge manually.
Inconsistent Data Modeling Standards
Inconsistent data modeling patterns, naming conventions, and architecture standards across development teams and projects. Without a unified approach, every new data domain is built differently. Medallion layer definitions, surrogate key strategies, SCD handling, and pipeline patterns vary from project to project, creating long-term maintenance and governance debt.
Missing Metadata Blocking Design
Missing or incomplete source metadata preventing accurate analytical layer design, dimensional modeling, and KPI alignment. Teams spending weeks in business analyst interviews trying to understand source data structures, relationships, and business rules before architecture work can even begin.
DDL and Pipeline Code Consuming 60-70% of Time
Manual DDL creation, ETL pipeline development, and data transformation coding consuming 60-70% of total project timelines for every greenfield build. The most repetitive, pattern-driven work in data engineering is still being done by hand, object by object, pipeline by pipeline.
Scarce Architecture Expertise
Lack of Distinguished-level data architecture expertise for optimal platform design, data model strategy, and performance optimization. Most teams don't have access to senior architects who understand medallion architectures, incremental load patterns, and platform-specific best practices for Snowflake, Databricks, Fabric, or BigQuery. Projects are designed by engineers who build what they know, not what's optimal.
Intelligent architecture. Not template generators.
3X Forward Engineer combines multi-source input analysis, intelligent metadata inference, and graph-based understanding with Distinguished Architect-grade AI to transform source data specs and KPI requirements into layered architecture, data models, and production-ready code. Not boilerplate schemas that need weeks of rework.
Multi-Source Input Analysis
Ingests and analyzes any combination of source data specs: API specifications and endpoints, CSV/Excel schemas, JSON and flat files, source database DDLs, and business objectives with KPI requirements. Automatically detects structure, data types, cardinality, relationships, and quality patterns across all source inputs simultaneously to build a unified understanding before architecture design begins.
KPI-Driven Architecture Design
Transforms business objectives and KPI requirements into optimized layered architecture with Medallion, Data Vault, Kimball, or hybrid modeling patterns. Every data model, dimension, fact table, and transformation is designed to directly support the dashboards, reports, and analytical outcomes stakeholders need. Architecture follows business intent, not generic templates.
Intelligent Metadata Inference
AI-powered metadata generation that infers business meaning, data classifications, column-level descriptions, and semantic relationships from attribute names, data patterns, and contextual signals. Eliminates weeks of manual documentation and business analyst interviews. Every object gets accurate, enriched metadata before modeling begins, not after.
Graph-Based Source Understanding
Deep source structure analysis using graph intelligence to automatically map cross-source data flows, implicit join paths, hidden dependencies, and business entity relationships. Transforms disconnected source inputs into a structured relationship map that provides the foundation for accurate multi-layer platform design across landing, staging, and analytical layers.
Complete Multi-Layer Architecture
GGenerates end-to-end layered data platform architecture: landing layer (raw ingestion), staging layer (cleansed and conformed), and analytical layer (KPI-aligned Medallion, Data Vault, or Kimball models) with transformation logic, SCD handling, incremental load strategies, and surrogate key management defined for each layer. Distinguished Architect-level solution design, delivered automatically.
Production-Ready Code and DDL Generation
AI-generates complete DDL for all tables across every layer, ETL pipeline code in SQL, PySpark, and Python, data quality validation scripts, lineage documentation, and orchestration templates for Snowflake, Databricks, Fabric, or BigQuery. Deployment-ready output with proper naming conventions, indexing strategies, and platform-specific performance optimizations. No manual coding gap between design and implementation.
From source specifications to production-ready platform.
A multi-stage intelligent pipeline that analyzes, designs, and generates your entire data platform automatically.
- Missing Metadata
- Manual Architect Effort
- Inconsistent Modeling Standards
- Months of Design & Build
Build for any modern cloud. Optimized natively.
Code and schemas generated specifically for your target platform — leveraging platform-specific features and best practices.
Snowflake
SQL, Snowpark, stages, tasks
Databricks
Spark SQL, Delta Lake, Unity
BigQuery
Standard SQL, BigQuery ML
Microsoft Fabric
Synapse, OneLake, pipelines
Production-ready platforms. From day one.
Everything your team needs to design, build, and deploy a modern data platform from source data specs to production-ready code, delivered automatically. No months of manual architecture. No inconsistent modeling. No waiting for scarce senior architects.
Instant Data Model Generation
Transform source data files and KPI requirements into complete dimensional models, Data Vault schemas, or Medallion architectures in hours with zero manual modeling. Every table, relationship, grain definition, and transformation logic designed by Distinguished Architect-grade AI that understands your data and business intent.
Production-Ready Code Library
Receive complete DDL scripts, ETL pipeline code (SQL, Python, PySpark), data quality validation scripts, lineage documentation, and deployment templates tailored to your exact source data, target platform, and modeling pattern. Not generic boilerplate. Code that deploys and runs on day one.
Embedded Architect Expertise
Distinguished-level architectural knowledge (15+ years equivalent) embedded in every design decision: performance optimization, scalability planning, SCD handling, incremental load strategies, partitioning schemes, and platform-specific best practices. Every team gets senior architect-grade output regardless of team seniority or budget.
KPI-Optimized Data Models
Analytical layers purpose-built for your specific KPIs and business objectives. Every fact table, dimension, measure, aggregation, and grain definition designed to directly support reporting, dashboards, and business intelligence requirements from day one. No post-build rework to align data models with what the business actually needs.
Automated Discovery and Assessment
Eliminate weeks of source system analysis, metadata gathering, and business requirement workshops. AI-powered discovery automatically understands source data structures, infers business context, maps relationships, and assesses complexity. Teams go from raw source specs to architecture-ready insights without manual investigation.
Platform-Tailored Implementation
Every schema, pipeline, and code artifact optimized specifically for your target platform: Snowflake warehouse clustering, Databricks Delta Lake optimization, BigQuery partitioning strategies, or Fabric Lakehouse and Warehouse patterns. Not platform-generic output. Code and architecture that leverages the specific performance features and best practices of where your data will live.
See Forward Engineer in Action
Get a personalised walkthrough tailored to your data engineering needs and greenfield platform challenges.
Let's talk scale.
Our team of engineering experts and AI architects is ready to help you accelerate your data modernization journey.