Data Engineer

About Us

1GLOBAL is a technology-driven global mobile communications provider helping enterprises and consumer brands deliver seamless connectivity worldwide. Powered by a best-in-class telecom platform, proprietary eSIM technology, and its own mobile core network across 15 countries, 1GLOBAL operates as a fully regulated telecommunications provider in 43 countries.

Founded in 2022, 1GLOBAL has rapidly become one of Europe's fastest-growing telecom technology companies, connecting more than 80 million people and devices globally. Our customers include leading banks, multinational enterprises, global retailers, travel companies, payment service providers, and digital-first businesses.

Headquartered in the Netherlands, with R&D hubs in Lisbon, Berlin, and São Paulo, 1GLOBAL employs over 550 professionals across 16 countries. With annual revenues exceeding US$200 million and a strong track record of profitability, we continue to invest in innovation, infrastructure, and international expansion as we redefine the future of global mobile connectivity.

About the Team

The Data Engineering team sits within the Central Data function, reporting to the Head of Data. We own the full data value chain: ingestion from source systems (billing, CRM, network, product, support, compliance), the data lake and warehouse layers, data quality and freshness. We don't own what the numbers mean — that's the business's call — but we own making sure the numbers are correct, timely, and traceable back to source.

You'll join Data Engineering as a hands-on engineer working on our core lake platform — ingesting high-volume network and customer data, modeling it into clean, reliable datasets, and making it available for reporting and self-service analytics across the business.

About the Role

Develop and implement data models, ensuring data accuracy, consistency, and reliability across raw and curated layers

Create and optimize ETL/ELT pipelines that ingest data from source systems (billing, CRM, network, product, support) into our S3-based data lake

Consume from and build reliable logic around Kafka topics, handling late, duplicate, or out-of-order events as they arrive

Own data transformations in dbt, with tests and documentation so trust in the numbers is built in, not bolted on

Orchestrate and monitor pipelines in Airflow, including retries, alerting, backfills, and SLAs

Write efficient, well-structured SQL for transformation logic and ad-hoc investigation

Contribute to data quality checks — completeness, freshness, reconciliation against source — and flag anomalies before they reach reporting

Collaborate with team members to ensure smooth development processes and shared understanding.

Take ownership of the quality and outcomes of your work, ensuring fully functional end-to-end flows.

Create and maintain accurate, up-to-date technical documentation.

Actively contribute to team discussions, with a mindset for continuous improvement and openness to new ideas or technologies.

Requirements

About You

Must Have

3-5 years of hands-on data engineering experience

Strong SQL — comfortable with window functions, CTEs, query optimization

Solid experience building and maintaining data pipelines on AWS S3, Glue, and Athena (or a closely comparable lake/warehouse stack — e.g. Spark + Hive/EMR, BigQuery, Redshift, Snowflake, Exasol — if you know the S3/Glue/Athena equivalents well)

Practical understanding of data modelling: raw → conformed layers, dimensional modelling basics, thinking about how a table will actually be queried

Experience consuming from Kafka (or another streaming/message system) as a data source

Advanced proficiency in Python for ETL development, data manipulation and system automation

Strong communication skills

Nice to Have

Airflow — DAG design, retries, backfills

dbt — modelling, testing, documentation

Exposure to Apache Iceberg or other open table formats

Experience in telecom or ano