Job Description

Location:

Northampton

Salary:

24,000 - 34,000 depending on experience

Company:

Cybist Ltd

About Us

Cybist Ltd is a fast-growing technology business based in Northampton, specialising in the refurbishment and resale of premium IT equipment. We're now building a new

SQL-based business system

to drive real-time profitability tracking, automation, and AI-driven insights.

We're now looking for a super capable and very ambitious to learn

Data Analyst / SQL Developer

to work on this system.

To start you will be reviewing work from an external developer, ensure the data logic and structures are sound, and help design future functionality including automation, reporting dashboards, and early AI integrations.

The Role

You'll sit at the intersection of data analysis, SQL development, and light front-end system design -- perfect for someone who loves solving practical data problems and learning fast.

You'll:

Review

existing SQL structures, views, and stored procedures for correctness and efficiency.

Validate and test

front-end screens (Retool / Appsmith style) against business logic and original database design.

Collaborate

with a senior developer to implement fixes and improvements.

Assist in automation

-- using SQL, Python, or low-code tools to streamline workflows.

Support dashboard development

for KPIs, profit & loss, and operational metrics.

Experiment with AI integration

, helping the business explore practical machine learning and predictive analytics applications.

Ideal Candidate

You'll have:

Solid

SQL skills

(queries, joins, views, stored procedures). Experience or interest in

data analysis

, reporting, or business systems. Some familiarity with

low-code or web app tools

(Retool, Appsmith, PowerApps, or similar). Exposure to

Python

,

automation scripts

, or

AI/ML tools

is a bonus. A methodical, detail-oriented approach and the confidence to evaluate others' work constructively. You will be precise and accurate in all of your work

What this offers:

Direct impact on live business systems used daily across operations. Real world SQL AI/automation actual experience not just theory. Exposure to Data Engineer, System Developer, or AI Automation roles. Entrepreneurial environment with freedom to contribute ideas and shape future systems.

How to Apply

Send your CV and a brief note about your experience with SQL, data analysis, or automation (academic, personal, or professional). If you've ever built or improved a data-driven system, we'd love to hear about it.

Final note/ Top Tip: If you re-read the final paragraph above and then follow it :) ---- then you are off to a head start over others in your application!

Thanks for your interest - Team Cybist!

BACKGROUND INFORMATION

Current Project Overview - being worked on by an external contractor now

Replace the locally server hosted FileMaker DB with a cloud-hosted Digital Ocean MS SQL database and a custom internal web app built with React (Tailwind UI + chart library) and a thin API (either .NET minimal APIs or Node/NestJS). The UI must equal or improve current FileMaker workflows and lay clean foundations for future automation/real?time P&L (Section 4, out of scope now). It will be kept in mind a later transition to Azure from DO is likely.

Why this approach

UX/control: Match FileMaker flows & keyboard-lean processing; no platform constraints.

Ownership: No vendor lock-in or per-seat fees; code in your repo, CI/CD ready.

Section 4-ready: API-first design supports automations, real-time metrics, SP-API, Linnworks.

Performance: Fine-grained tuning (indexes, cached views, virtualized grids).

Security: Self-host or VPC-only; add Azure AD/OIDC later without rework.

Section 1 -- Core Migration to MS SQL

Goal: Normalize FileMaker data & logic to MS SQL with a documented ERD; prepare views/endpoints for the new app.

Deliverables

MS SQL environment: provisioned, roles & least?privilege access. Implement DB-level RBAC (engineer, manager, admin) now, using SQL-native authentication on DigitalOcean. Schema and API must be designed so SSO (Azure AD / AWS IAM) can be added later without rework.

ERD & logical model based on exports/screenshots.

Normalized schema with PK/FK, essential non?clustered indexes, core views.

Migration: scripts (CSV/Excel ? staging ? cleansed ? final); reconciliation report.

Reference data: suppliers, engineers, statuses, reason_codes, brands, categories, sales_channels.

Documentation: schema guide; migration run-book; backup/restore checklist.

Acceptance

Row counts & FK constraints validated.

Standard lookups on sample volumes return sub?second.

Backup & one restore test documented.

Section 2 -- Custom Internal Web App (React + API)

Goal: Deliver an internal app matching or improving FileMaker usability with a thin API layer.

Frontend

React (Vite or Next.js), Tailwind CSS, component library.

Pages/Screens: Goods?In, Production Worksheet, Dispatch/Shipments, Search & Record View.

UX: keyboard shortcuts; click?parity or better vs FileMaker; validation on save.

API

Choice: .NET minimal APIs (MSSQL fit) or Node/NestJS (JS-first).

Illustrative endpoints: GET /units, GET /units/{id}, PATCH /units/{id}, POST/GET goods?in, GET/POST shipments, GET engineers, GET production queues, GET kpi/{metric}.

Auth: internal trusted users now with DB-level RBAC. Pluggable OIDC/Azure AD or AWS IAM later, without schema/API rework.

Validation & audit: server?side validation; created_by/updated_by/timestamps.

Acceptance

Workflows complete with same or fewer clicks vs FileMaker.

Required fields enforced; audit fields recorded.

Responsive desktop experience; restricted to internal users.

Section 3 -- KPI Dashboards & Reports

Goal: Ship core visibility with exportable reports powered by the API.

KPIs

Lead time: goods?in ? finished/ready?to?ship/shipped (unit & batch).

Engineer throughput: units/engineer/day/week; returns rate (if available).

Shipment volume: daily/weekly; slice by brand/category/supplier.

Basic batch profitability: purchase + logged work/fees vs revenue (where available).

Deliverables

React KPI views (charts/tables) using Recharts or Chart.js.

Exports: CSV/PDF (server?side export endpoint); optional Google Sheets later.

Docs: KPI definitions; SQL/view logic; endpoint specs.

Acceptance

KPI figures match sample calculations.

Exports work (manual trigger; cron optional later).

Inputs We Provide

FileMaker exports (CSV/Excel), layout screenshots.

Sample KPI definitions and hand?check calculations.

Cloud MS SQL credentials; internal environment for app/API.

Named contacts for decisions & UAT.

Out of Scope (now)

Automation, real?time P&L, pricing, forecasting (see Technical Backdrop).

Third?party APIs (Amazon SP?API, Linnworks, Repricers) beyond preparing schema/API surfaces.

Public/external UI; mobile optimization.

Non?Functional

Security: DB-level RBAC with least?privilege roles. Secrets via env/KeyVault; no secrets in repos. Roles/permissions implemented at DB layer and enforced in API. Design kept portable for Azure AD/AWS IAM integration later.

Backups: daily automated; one restore test documented.

Performance: hot paths indexed; list views feel instant; dashboard tiles pre?aggregated daily.

Docs: ERD, schema dictionary, endpoint OpenAPI, migration/backup run?books.

DevOps: repo with CI (build/lint/tests), CD to staging/prod; env parity.

Minimal Performance Standards

Indexes: PK + hot filters (Order_Number, Serial_Number, Status).

Lookups: sub?0.5s feels instant.

Dashboards: initial load <1s on current data via pre?aggregated tiles.

No foot?guns: any slow query at handover has an implemented fix.

Housekeeping: daily backups; restore test passes.

Job Types: Full-time, Part-time, Permanent, Temporary, Temp to perm, Freelance, Graduate

Pay: 24,000.00-30,000.00 per year

Ability to commute/relocate:

Northampton NN1 5LP: reliably commute or plan to relocate before starting work (preferred)
Work Location: In person