GT was founded in 2019 by a former Apple, Nest, and Google executive. GT’s mission is to connect the world’s best talent with product careers offered by high-growth companies in the UK, USA, Canada, Germany, and the Netherlands.

On behalf of the client, GT is looking for a Data Engineer who is interested in working with big data.

About the Client

Our client is a leading global management consultancy known for tackling some of the world’s most complex business challenges. With a focus on strategy, transformation, and performance improvement, the firm partners with major organizations across industries to drive lasting impact.

Recognized consistently as a top workplace, it combines deep industry expertise with a collaborative, innovative culture. Its centralized European hub plays a key role in supporting operations across the EMEA region, ensuring excellence and efficiency at scale.

About the Project & Role

The Data Engineer will play a critical role in developing, maintaining, and scaling the pipelines and data systems that power the client's unified data platform. Working alongside data architects, product managers, and analysts, this role is focused on ingesting, transforming, and enriching firmographic data from multiple third-party and proprietary sources.

The engineer will support data operations across 2 ecosystems, contributing to the client's mission of building the best business directory in the world and enabling differentiated, data-driven insights for consultants and clients.

Contract duration: 6 months, with the possibility of extension.

Key Responsibilities

Build Data Pipelines: Design and maintain robust, scalable ETL/ELT pipelines to ingest and process third-party and first-party datasets.
Data Quality & Enrichment: Apply transformation, normalization, and enrichment rules to ensure data consistency and usability.
Collaborate Across Teams: Work with product managers, data architects, and content experts to align data structure with business needs.
Operationalize Matching & Merging Logic: Support the implementation of data matching and entity resolution processes using AI/ML tools and proprietary frameworks.
Monitor & Troubleshoot Pipelines: Build alerts, logs, and metrics to ensure data flows remain healthy and issues are identified and resolved quickly.
dbt Development: Build and maintain data pipelines using dbt, developing models from raw data through staging and intermediate layers up to final semantic models for use in analytics and dashboards.
Documentation & Standards: Contribute to documentation, code quality standards, and internal best practices to ensure maintainability.

Ideal Candidate Profile

Experience: 4–8 years in data engineering, with experience in building production-grade data pipelines.
Tech Stack: Proficient in SQL, Python, and dbt, with strong experience in Snowflake. Additional experience with Spark, Airflow, and Azure Data Lake or similar technologies.
Cloud Platforms: Familiarity with Azure (preferred) or other major cloud platforms.
Data Engineering Best Practices: Understanding of data modeling, version control, CI/CD, and data governance principles.
Curiosity & Ownership: Proactive, detail-oriented, and eager to take ownership of projects and continuously improve systems.

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