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.
---
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
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.
---
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