Senior Data Engineer

Role overview Design and operate large-scale data pipelines that organize company-year workforce observations from millions of companies across many countries. You’ll work with engineering and product colleagues to make the data reliable, searchable, and usable in structured exports, while supporting governance and privacy requirements.

Responsibilities - Build and maintain ETL/ELT pipelines for ingesting and aggregating workforce data. - Improve data storage and processing for datasets at terabyte scale. - Create quality checks for data accuracy, consistency, and completeness. - Develop normalization, deduplication, and entity-resolution methods across sources. - Deliver structured exports and clear definitions for the metrics they contain. - Add monitoring and alerting for pipeline operations, and contribute to GDPR-aligned data governance.

Requirements - At least five years of experience in data engineering or a related field. - Strong Python and SQL skills, plus experience with Spark, Airflow, or comparable tools. - Experience processing large datasets and working with distributed storage and columnar formats such as Parquet or ORC. - Familiarity with PostgreSQL, ClickHouse, or similar analytical databases. - Knowledge of data modeling, schemas, and normalization. - Experience with cloud infrastructure or bare-metal environments.

Nice to have - Experience with record linkage, entity resolution, or identity graphs. - Background in B2B data, professional networks, or workforce analytics. - Familiarity with GDPR in data workflows or tools such as dbt and Great Expectations.

Benefits and work setup The role is full-time and remote, with a preference for candidates in EU time zones. The source mentions equity participation, annual team gatherings in Europe, and a learning and development budget.