Senior Data Engineer

We are looking for an experienced Senior Data Engineer (L3) to join our Data Science team and build scalable, high-performance data platforms that power machine learning models, analytics, and business intelligence. In this role, you will work closely with Data Scientists and ML Engineers to design robust data models, develop production-grade data pipelines, and optimise large-scale data processing systems using modern cloud-native technologies. If you're passionate about data engineering, distributed systems, and AI-enabled workflows, we'd love to hear from you.

Responsibilities:

• Design, develop, and maintain DBT models to deliver trusted datasets, metrics, and feature tables for analytics and machine learning.
• Build and manage scalable Databricks pipelines using PySpark, Delta Lake, and Iceberg.
• Develop reliable end-to-end ETL/ELT workflows using Airflow and Prefect.
• Partner with Data Scientists and ML Engineers to productionize feature engineering pipelines.
• Create scalable data architectures that transform raw operational data into business-ready datasets.
• Optimise Spark jobs, DBT models, and SQL queries for performance, reliability, and cost efficiency.
• Ensure high standards for code quality through testing, documentation, and peer code reviews.
• Design and maintain data models aligned with business operations and analytics requirements.
• Build resilient data pipelines with monitoring, scheduling, and SLA management.
• Collaborate with cross-functional engineering teams in a fast-paced Agile environment.

Requirements:

• 8+ years of experience in Data Engineering or Big Data development.
• Strong expertise in SQL.
• Hands-on experience with one or more of: DBT, PySpark, Apache Spark, Airflow
• Strong experience working with Databricks.
• Experience building scalable distributed data systems.
• Knowledge of Delta Lake and/or Apache Iceberg.
• Experience with Infrastructure as Code tools such as: Terraform, AWS CDK, Pulumi
• Good understanding of distributed computing concepts including consistency, fault tolerance, throughput, and latency.
• Strong communication skills with experience collaborating across global teams.
• Experience using AI-powered development tools such as GitHub Copilot, Cursor, Claude Code, or similar AI coding assistants.

Good to Have:

• Experience with Lakehouse Architecture.
• Knowledge of Kinesis or other streaming platforms.
• Exposure to MLOps or Machine Learning infrastructure.
• Experience supporting AI/ML teams.
• Prior experience working with US-based engineering teams.
• Experience integrating LLMs or AI capabilities into data platforms or internal tooling.