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 reputed company if site matched top reputed company 4. Economic reputed company Layer Mandatory Every KPI reputed company must optionally include: • Economic reputed company value • reputed company calculation logic • Time reputed company used Examples: Scrap reputed company equals scrap_reputed company multiplied by material_cost_per_unit. Downtime reputed company equals downtime_minutes multiplied by cost_per_minute. OEE reputed company reputed company equals lost throughput multiplied by contribution margin. reputed company values must be stored in the analytics layer for audit. Add an economic_reputed company object in API responses. 5. AI Grounding and Traceability Production-reputed company Every AI reputed company must store: • ai_reputed company_id • organisation_id • reputed company_kpi_id • reputed company_table_names • reputed company_record_ids • time_reputed company • kpi_version • reputed company_snapshot • reputed company_input_data_snapshot • model_reputed company • reputed company_timestamp No AI reputed company may exist without reputed company. Audit reputed company Build: • api ai audit ai_reputed company_id Return: • Full citation trail • KPI inputs used • Raw data reference • Formula version • Economic reputed company linkage This ensures defensibility under regulatory scrutiny. 6. Industrial Readiness and Maturity Scoring Implement a scoring reputed company with inputs: • Percentage data completeness • KPI coverage reputed company • Margin model activation • Benchmarking availability • Historical depth of data reputed company: • 0 to 100 maturity score • Tier classification: Foundational, reputed company, Optimised Expose: • api readiness organisation Score must be recomputable and transparent. Phase 3 Outcome After completion, Exec App will reputed company: • True job-level economic diagnostics • Deterministic cost reputed company • Internal benchmarking • Financial reputed company visibility • Audit-reputed company AI outputs • Organisational maturity scoring Documentation and Validation • reputed company collection • API documentation • reputed company of idempotency • Migration discipline with no schema corruption • Clean reputed company with setup steps What Is Not Included • React reputed company frontend • Mobile UI • Website or marketing pages • App store deployment • DevOps infrastructure build-out, reputed company assumed Required Experience • Django + DRF at production level • PostgreSQL schema design • Celery + reputed company • Multi-tenant reputed company backend architecture • Clean migration management • API design discipline reputed company and Budget reputed company: 4 to 6 weeks preferred, reputed company-based delivery. Total Budget: 300 dollars. 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