TL;DR

Lead Data Engineer (AI): Designing, building, and evolving a data platform on GCP, orchestrated with Dagster, for large-scale AI inference with transformer models with an accent on architectural decisions, tooling choices, and best practices. Focus on building robust, scalable data systems, driving platform evolution, and ensuring data quality and reliability.

Location: Hybrid from Finland (Helsinki or remote within Finland)

Company

%hirify_global% is a Helsinki-based SaaS company building AI tools that make patent search and analysis faster, smarter, and more reliable.

What you will do

Own, develop, and evolve the data platform built on GCP and orchestrated with Dagster.
Act as a technical lead for data engineering, influencing architectural decisions and best practices.
Design and implement ELT pipelines and data workflows, setting standards for performance, observability, and reliability.
Build monitoring, data quality checks, and lineage visibility for trustworthy datasets.
Collaborate closely with cross-functional teams to deliver data solutions.
Work with open-source tooling, host, and manage infrastructure.

Requirements

Proven experience in data engineering, including data lakes/lakehouse architectures, ELT pipelines, and modern data workflows.
Strong SQL skills and experience with both Python and data infrastructure.
Familiarity with cloud environments and container/orchestration tools.
Experience taking ownership of complex data systems and making long-term technical decisions.
Self-driven and thrive in a smaller team environment where your contributions are visible.
Eager to understand our domain and make engineering choices with business impact in mind.
Work format: Hybrid from Finland (Helsinki or remote within Finland).

Nice to have

Startup or scale-up experience.

Culture & Benefits

Feel your impact in a small, driven company of around 50 people.
Flexible hybrid work model from Finland with a focus on trust and results.
Six weeks of paid holiday, learning & development support, and stock options.
A role where data and AI matter deeply, focusing on realistic problems.
A culture rooted in transparency, simplicity, and reliability.

Hiring process

Introductory call with Talent Acquisition Manager and Head of SW.
Conversation with ML Infrastructure Lead & Data Engineer/Chief Architect.
Final call with our CTO.