Data Engineer

Location: Hybrid in London, United Kingdom or Zagreb, Croatia

Company

hirify.global builds an AI-powered tax operating system covering tax ID validation, real-time tax determination, e-invoicing, and tax returns across more than 120 countries.

What you will do

• Design, build, and iterate on the foundational internal data platform.
• Evaluate tools and technologies for the required scale, capabilities, and data workloads.
• Connect data from internal systems and services into a unified, queryable layer.
• Build interfaces and tooling that enable Product, Finance, and Operations stakeholders to access data independently.
• Establish data modelling, cataloguing, documentation, governance, quality, validation, and monitoring standards.
• Partner with Infrastructure and business stakeholders to discover high-impact data opportunities and shape the data roadmap.

Requirements

• 5+ years of experience in Data Engineering or a similar role, such as Analytics Engineering or Backend Engineering with substantial data work.
• Strong SQL skills and proficiency in Python, Scala, or a similar programming language.
• Experience building and maintaining ETL/ELT pipelines and working with data warehouses or lakehouse architectures.
• Hands-on experience with a columnar warehouse such as BigQuery or Snowflake, a transformation layer such as dbt, and an ingestion or CDC tool such as Airbyte or Fivetran.
• Familiarity with cloud platforms, preferably GCP, data services, and workflow orchestration tools.
• Strong communication skills, pragmatic decision-making, and the ability to translate non-technical stakeholder needs into reliable data solutions.

Nice to have

• Experience with Metabase, Looker, Superset, or similar data visualization and BI tools.
• Background in a product-oriented SaaS company.
• Experience with financial or operational data, including revenue, billing, or reconciliation.
• Exposure to infrastructure-as-code and DevOps practices.

Culture & Benefits

• Work in small, high-trust teams with ownership across the full engineering lifecycle.
• Focus on solving customer problems, simplicity, quality, speed, iteration, and collaboration.
• Collaborate with Infrastructure, Finance, Operations, and Product teams.
• Build a data function from the ground up in an environment that values curiosity, proactivity, and comfort with ambiguity.

Hiring process

• Engineering candidates are evaluated partly on API design and structure.
• Specific tools are not mandatory; evaluation focuses on understanding problems, making sound technical decisions, and selecting appropriate technologies.