WORKING PORTFOLIO · CAPE TOWN, SOUTH AFRICA

Civil engineering meets data analytics.

Yatify is the working portfolio of Nyashadzashe Munyati — a Civil Engineering Technologist combining resident-engineering experience with project controls, engineering information reporting, SQL, Power BI-ready modelling, APIs and construction data analytics.

NYASHADZASHE MUNYATICivil Engineering Technologist · Construction Data AnalyticsResident-engineering experience since 2020 · Advanced Diploma in Civil Engineering (NQF Level 7)

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SELECTED PORTFOLIO WORKEvidence you can inspect

Full portfolio

PR Project Controls Performance Lab19,200 schedule snapshot rows + 800 activities simulation› IN Engineering Information Performance Lab52,000 synthetic events simulation› DA Data Quality & UAT Lab22 validation tests across 3 analytical areas published› CO The Bitumen Effect13 civil activity series + 31 input products published›

SQLDAXPower BIProject ControlsUATAPIs

ENGINEERING INFORMATION52,000RFI, submittal & deliverable events

PROJECT CONTROLS19,200schedule snapshots · 800 activities

DATA QUALITY / UAT22inspectable validation tests

ANALYTICAL LOGICSQL + DAXdownloadable queries and measures

WHAT THE PORTFOLIO DEMONSTRATES

Engineering knowledge translated into analytical work.

Each area is backed by a detailed project, data structure, analytical logic and evidence rather than a software-logo skills list.

PROJECT CONTROLS & REPORTING

Schedule, progress and performance analysis

WBS/packages, monthly snapshots, planned vs actual progress, float erosion, milestones, resource hours and S-curve-ready history.

19,200 rows · SQL · DAX · Power BI-ready model · UAT

ENGINEERING INFORMATION

RFI, submittal and deliverable analytics

Ageing, cycle times, first-pass approvals, overdue workload, package performance and discipline bottlenecks.

52,000 rows · SQL window functions · DAX · data dictionary

DATA QUALITY & UAT

Proving the report can be trusted

Uniqueness, completeness, range, date and referential-integrity tests with expected result, actual result, severity and PASS/WARN/FAIL.

22 validation tests · publication gates · reconciliation

DATA PIPELINES & MARKET INTELLIGENCE

APIs, SQL schemas and construction intelligence

OCDS ingestion architecture, explainable classification, public construction data and engineering-led market questions.

REST / JSON · PostgreSQL · public procurement · Stats SA

SELECTED CASE STUDIES

Open the work, not just the résumé.

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Case studyWhat it demonstratesEvidence

Project Controls Performance LabProject Controls · simulation Identify package slippage, float erosion, milestone risk and resource variance early enough for a project team to investigate and recover the programme. Dataset · SQL · validation Engineering Information Performance LabInformation Management · simulation Detect information bottlenecks before they become programme or coordination problems. Dataset · SQL · validation Data Quality & UAT LabData Quality · published Determine whether an analytical output is safe to publish and identify exactly which rule failed when it is not. Dataset · SQL · validation The Bitumen EffectConstruction Materials · published Choose the most relevant price signal for road-material exposure instead of relying on one blended construction headline. Dataset · SQL · validation Civil Infrastructure Tender Intelligence EngineProcurement Analytics · pipeline-ready Convert procurement records into a reusable, queryable infrastructure pipeline instead of manually checking tender pages one by one. Dataset · SQL · validation

DATA YOU CAN TRACE

Public sources stay attached to the analysis.

Yatify records where a dataset came from, what period it covers and whether a product is live, simulated, research-ready or still waiting for validated data.

Open Data Lab

MonthlyStats SA P0151.1Release + time-series source

IncrementalNational Treasury eTender OCDS APIREST API

Quarterly / reporting cycleNational Treasury Municipal FinancePublic data portal

OngoingCity of Cape Town Tender PortalPublic tender portal

MonthlyStats SA P0142.1 — PPI fuel & petroleum inputsRelease + time-series source

HOW IT WORKS

Collect. Validate. Analyse. Publish.

Automation helps with scale; it does not replace validation.

Collect

Public source, API, spreadsheet or clearly labelled synthetic data.

Validate

Freshness, schema, missing values, duplicates and revisions.

Analyse

SQL, Power Query, time-series logic and transparent classification.

Publish

Decision, evidence, method and limitations appear together.