Intermediate / Senior Data Engineer

Our client is a leading global provider of legal, regulatory and business information and analytics that help customers increase productivity, improve decision-making and outcomes, and advance the rule of law around the world.

They help lawyers win cases, manage their work more efficiently, serve their clients better and grow their practices. They assist corporations in better understanding their markets, monitoring their brands and competition, and in mitigating business risk. They collaborate with universities to educate students, and support nation-building with governments and courts by making laws accessible and strengthening legal infrastructures. They partner with leading global associations and customers to collect evidence against war criminals and provide tools to combat human trafficking.

Responsibilities:

• Designing, implementing, and optimising scalable data pipelines using Python, PySpark, and Databricks.
• Driving the delivery of customer-facing data products through APIs, Databricks-based sharing, and event-driven mechanisms (e.g., Kafka or similar).
• Taking ownership of end-to-end features — from scoping and development to deployment and monitoring.
• Leading and participating in technical design discussions, contributing to architectural improvements and long-term data strategy.
• Mentoring and coaching junior engineers actively while supporting their technical and professional development.
• Collaborating with cross-functional teams to understand business requirements and translate them into technical solutions.
• Conducting peer code reviews, upholding code quality standards, and championing continuous improvement practices.
• Contributing to the team’s agile delivery process and provide input during planning and retrospectives.

Requirements:

• Have extensive experience in data engineering or backend software engineering with a data focus.
• Be proficient in Python, PySpark, and working within Databricks environments.
• Have hands-on experience designing and delivering data products via REST APIs, event-driven systems, or data sharing platforms like Databricks Delta Sharing.
• Have a solid understanding of distributed data processing, ETL/ELT workflows, and data lake/lakehouse architectures.
• Have experience with cloud platforms (e.g., AWS, Azure, GCP) and modern data tooling.
• Have a proven track record of mentoring or coaching other engineers.
• Have effective communication and collaboration skills, including the ability to explain complex concepts to non-technical stakeholders.
• Be familiar with agile development practices