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 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. Apply tot his job Apply To this Job