Job Overview

Job Type

Full Time

Pay

Not disclosed

Apply Now

Job description

job description

We are building a modern analytics and Business Intelligence solution for customers in the temp-staffing industry, integrating operational data from multiple ERP systems across countries into reliable, customer-facing insights, analytical workflows, and reusable data products.

This is not a traditional data analyst or classic BI developer role . We are looking for a product-minded fullstack engineer with a strong data focus: someone who can move from messy ERP data and product-defined KPIs to validated datasets, pipelines, APIs, internal tools, and dashboards where needed.

AI and LLM tooling are central to how we work. We expect someone who uses AI-native workflows to explore faster, build in parallel, validate assumptions, and ship high-quality production solutions.

What We’re Looking For
We are looking for a fullstack engineer with a strong data focus . You should turn ambiguous problems into working software, use AI as a default development workflow, care about correctness and maintainability, understand data edge cases, choose simple robust solutions, own the outcome from exploration to production, and move quickly while verifying aggressively.

your tasks

Fullstack Product Engineering: Build backend services, APIs, internal tools, lightweight UI/admin screens, automation, job runners, integrations, and customer-specific configuration around the data

Data Pipeline & Modeling: Ingest, validate, transform, and document ERP, API, SQL, file, and cloud data; map product-defined KPIs to available sources and identify gaps or inconsistencies

Curated Data Products: Create validated, analysis-ready datasets with consistent schemas, reproducible transformations, and clear naming for reporting, APIs, product features, and customer-facing analytics

Cloud & Production Ownership: Deploy and operate reliable cloud solutions, preferably AWS, owning monitoring, alerts, failure handling, performance, cost, and operational reliability

your profile

AI-Native Development

Hands-on with Claude Code, Codex, and agent-based workflows; GitHub Copilot-style autocomplete alone is not enough

Familiar with worktrees, subagents, MCP, structured prompts, harness engineering, parallelization, and validating AI-generated code and analysis to production quality

Software Engineering

Strong fullstack/backend experience, ideally with Python and/or TypeScript

Able to build production-grade services, APIs, scripts, tools, automation, and clean interfaces; comfortable with version control, review, debugging, testing, and existing systems

Data Engineering & Analytics

Strong SQL, data modeling, analytical schemas, transformations, and downstream data use

Able to translate product-defined KPIs into datasets and metrics, and validate messy operational data, edge cases, system limitations, and customer-specific differences

Cloud & Infrastructure

Hands-on with AWS or similar cloud environments, including storage, databases, queues, containers, serverless/scheduled processing, SDKs, and APIs

Understands deployment, secrets, networking, permissions, runtime configuration, scalability, performance, cost, and operational trade-offs

Good Fit
You may be a good fit if you are a fullstack/backend engineer with strong data or analytics experience, a Python/TypeScript engineer who enjoys data products and automation, an analytics/data engineer with real software engineering depth, a technical founder/builder profile, or an AI-native engineer using LLMs and agents daily for production work.

Not a Good Fit
This role is probably not the right fit if you are mainly a dashboard-only BI analyst, classic report builder, pure data warehouse engineer waiting for predefined tickets, notebook-only analyst without production engineering experience, engineer with no interest in data modeling, someone who avoids ambiguity, or someone who does not actively use and rigorously validate AI-generated output.

your benefits

Collaboration in an empathetic, appreciative team with room to contribute ideas and take ownershipIndividual development opportunities, structured onboarding, and interdisciplinary collaboration

Flexible working models including hybrid work, home office, and mobile working