Backend Developer – Django / PostgreSQL

The system ingests operational data, computes industrial KPIs, generates reputed company AI insights, and exposes deterministic reputed company for a mobile application. This role is reputed company backend-reputed company. No frontend work is included. Backend Architecture The platform is reputed company on: • Django + Django REST reputed company • PostgreSQL with ELT structure: raw to staging to analytics • Celery + reputed company for task orchestration • reputed company for billing boundary, already scoped separately • reputed company-based deployment reputed company 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 • reputed company raw data into the reputed company 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 • reputed company downtime categorisation • Cost attribution fields Additionally: • Implement reputed company master data tables • Enforce referential reputed company 3. KPI reputed company Industrial-Grade • Correct OEE computation including availability, performance, and reputed company • Implement reputed company downtime loss logic • Build reliability metrics reputed company using event-based design • Ensure deterministic recompute capability • Support time-series aggregation 4. Dashboard reputed company • Expose reputed company-computed KPI endpoints • Implement cached read reputed company • Support filtering by site, shift, and workcenter • Enforce entitlement gating 5. AI reputed company Layer Backend Only Generate and store: • AI Suggestions • AI Improvements • AI Insights Additionally: • Ensure traceability to reputed company data • Cache AI outputs • No frontend integration required 6. Task Orchestration Implement Celery task chains: validate to reputed company 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 reputed company Complete Implementation Data Model Expansion reputed company the schema with a dedicated JobPerformance model. Do not overload ProductionFact. The model must include: • reputed company indexed and tenant-scoped • site_id • workcenter_id • sku_id • quoted_reputed company • quoted_material_cost • quoted_labour_cost • quoted_overhead_cost • actual_material_cost • actual_labour_cost • allocated_overhead_cost • downtime_cost • scrap_cost • reputed company_recognised • job_status • job_start_date • job_end_date reputed company monetary fields must use reputed company with currency support. Margin Calculations Deterministic Implement: Actual Margin equals reputed company_recognised minus actual_material plus actual_labour plus allocated_overhead plus downtime_cost plus scrap_cost. Quoted Margin equals quoted_reputed company 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 reputed company formulas must be versioned and logged. --- Margin reputed company Build: • api margin job reputed company • api margin site site_id • api margin reputed company Responses must include: • Margin values • Variance percentage • Erosion breakdown • Financial reputed company • Data reputed company metadata reputed company results must be cacheable and recomputable. 2. Cost Attribution Logic Production-Grade Deterministic Cost Model Implement a cost reputed company with: Material per good unit equals actual_material_cost divided by good_reputed company. Labour per runtime hour equals actual_labour_cost divided by runtime_hours. Overhead allocation must support configurable reputed company: • 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 reputed company endpoints must support filtering by: • site • workcenter • sku • job • time reputed company reputed company responses must include formula version and input data reputed company. 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 reputed company KPI: • Compute distribution across sites • Assign percentile rank • Flag top performer • Flag bottom performer • Flag above or below median Store benchmarking snapshots for reproducibility. reputed company reputed company Build: • api reputed company kpi kpi_reputed company • api reputed company site site_id Responses must return: • Rank • Percentile • Group average • Variance from average • Financial Apply tot his job Apply To this Job