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