Backend Developer – Django / PostgreSQL

The system ingests operational data, computes industrial KPIs, generates structured AI insights, and exposes deterministic APIs for a mobile application.

This role is strictly backend-focused. No frontend work is included.

Backend Architecture

The platform is built on:
• Django + Django REST Framework
• PostgreSQL with ELT structure: raw to staging to analytics
• Celery + Redis for task orchestration
• Stripe for billing boundary, already scoped separately
• Docker-based deployment

Core Architectural Principles
• Multi-tenant isolation at organisation and site level
• Deterministic KPI recomputation
• Append-only raw data layer
• Strict schema validation for ingestion
• Versioned KPI logic
• AI outputs must be grounded in stored data
• No autonomous AI actions, advisory only

Backend Responsibilities High-Level

1. Data Ingestion Layer
• Build a robust CSV ingestion pipeline
• Implement header validation and schema enforcement
• Ensure idempotent file handling with no duplicate ingestion
• Transform raw data into the canonical ProductionFact model
• Maintain ingestion logs and validation reports

2. Manufacturing Data Model Refinement

Refactor the ProductionFact schema to support:
• Workcenter context
• SKU and job granularity
• Structured downtime categorisation
• Cost attribution fields

Additionally:
• Implement canonical master data tables
• Enforce referential integrity

3. KPI Engine Industrial-Grade
• Correct OEE computation including availability, performance, and quality
• Implement structured downtime loss logic
• Build reliability metrics foundation using event-based design
• Ensure deterministic recompute capability
• Support time-series aggregation

4. Dashboard APIs
• Expose pre-computed KPI endpoints
• Implement cached read APIs
• Support filtering by site, shift, and workcenter
• Enforce entitlement gating

5. AI Insight Layer Backend Only

Generate and store:
• AI Suggestions
• AI Improvements
• AI Insights

Additionally:
• Ensure traceability to source data
• Cache AI outputs
• No frontend integration required

6. Task Orchestration

Implement Celery task chains:

validate to transform to ingest to compute KPIs to generate AI

Also include:
• Scheduled ingestion support
• Idempotent task handling

Phase 3 – Manufacturing Intelligence Expansion

1. Job-Level Margin Foundation Complete Implementation

Data Model Expansion

Extend the schema with a dedicated JobPerformance model. Do not overload ProductionFact.

The model must include:
• job_id indexed and tenant-scoped
• site_id
• workcenter_id
• sku_id
• quoted_revenue
• quoted_material_cost
• quoted_labour_cost
• quoted_overhead_cost
• actual_material_cost
• actual_labour_cost
• allocated_overhead_cost
• downtime_cost
• scrap_cost
• revenue_recognised
• job_status
• job_start_date
• job_end_date

All monetary fields must use Decimal with currency support.

Margin Calculations Deterministic

Implement:

Actual Margin equals revenue_recognised minus actual_material plus actual_labour plus allocated_overhead plus downtime_cost plus scrap_cost.

Quoted Margin equals quoted_revenue minus quoted_material plus quoted_labour plus quoted_overhead.

Margin Variance percentage equals Actual minus Quoted divided by Quoted.

Margin Erosion Attribution must break down percentage erosion into:
• Scrap contribution
• Downtime contribution
• Labour overrun
• Material price variance

All formulas must be versioned and logged.

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Margin APIs

Build:
• api margin job job_id
• api margin site site_id
• api margin summary

Responses must include:
• Margin values
• Variance percentage
• Erosion breakdown
• Financial impact
• Data lineage metadata

All results must be cacheable and recomputable.

2. Cost Attribution Logic Production-Grade

Deterministic Cost Model

Implement a cost engine with:

Material per good unit equals actual_material_cost divided by good_units.

Labour per runtime hour equals actual_labour_cost divided by runtime_hours.

Overhead allocation must support configurable methods:
• Per shift
• Per runtime hour
• Per job

A configuration table must define the allocation rule per tenant.

KPI Endpoints

Build:
• api kpi cost-per-unit
• api kpi cost-variance
• api kpi unit-economics

All endpoints must support filtering by:
• site
• workcenter
• sku
• job
• time range

All responses must include formula version and input data range.

3. Cross-Site Normalised Benchmarking Internal

Normalisation Rules

Standardise:
• OEE time-weighted
• Scrap percentage
• Cost per unit

Ensure:
• Comparable time ranges
• Comparable shift hours
• Currency normalisation

Percentile Logic

For each KPI:
• Compute distribution across sites
• Assign percentile rank
• Flag top performer
• Flag bottom performer
• Flag above or below median

Store benchmarking snapshots for reproducibility.

Benchmark APIs

Build:
• api benchmark kpi kpi_name
• api benchmark site site_id

Responses must return:
• Rank
• Percentile
• Group average
• Variance from average
• Financial delta if site matched top quartile

4. Economic Impact Layer Mandatory

Every KPI endpoint must optionally include:
• Economic impact value
• Impact calculation logic
• Time range used

Examples:

Scrap impact equals scrap_units multiplied by material_cost_per_unit.

Downtime impact equals downtime_minutes multiplied by cost_per_minute.

OEE delta impact equals lost throughput multiplied by contribution margin.

Impact values must be stored in the analytics layer for audit.

Add an economic_impact object in API responses.

5. AI Grounding and Traceability Production-Ready

Every AI output must store:
• ai_output_id
• organisation_id
• related_kpi_id
• source_table_names
• source_record_ids
• time_range
• kpi_version
• prompt_snapshot
• structured_input_data_snapshot
• model_name
• generation_timestamp

No AI output may exist without lineage.

Audit Endpoint

Build:
• api ai audit ai_output_id

Return:
• Full citation trail
• KPI inputs used
• Raw data reference
• Formula version
• Economic impact linkage

This ensures defensibility under regulatory scrutiny.

6. Industrial Readiness and Maturity Scoring

Implement a scoring engine with inputs:
• Percentage data completeness
• KPI coverage ratio
• Margin model activation
• Benchmarking availability
• Historical depth of data

Output:
• 0 to 100 maturity score
• Tier classification: Foundational, Structured, Optimised

Expose:
• api readiness organisation

Score must be recomputable and transparent.

Phase 3 Outcome

After completion, Exec App will provide:
• True job-level economic diagnostics
• Deterministic cost engine
• Internal benchmarking
• Financial impact visibility
• Audit-ready AI outputs
• Organisational maturity scoring

Documentation and Validation
• Postman collection
• API documentation
• Proof of idempotency
• Migration discipline with no schema corruption
• Clean README with setup steps

What Is Not Included
• React Native frontend
• Mobile UI
• Website or marketing pages
• App store deployment
• DevOps infrastructure build-out, Docker assumed

Required Experience
• Django + DRF at production level
• PostgreSQL schema design
• Celery + Redis
• Multi-tenant SaaS backend architecture
• Clean migration management
• API design discipline

Timeline and Budget

Timeline: 4 to 6 weeks preferred, milestone-based delivery.

Total Budget: 300 dollars. No negotiation. More work to follow.

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