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.