Senior Data Engineer

Senior Data Engineer

We're hiring aSenior Data Engineer to take technical ownership of a modern, AWS-native data platform, spanning everything from raw data ingestion and transformation to building scalable, reliable data pipelines that power production machine learning and AI systems.

This is a hands-on, high-autonomy role within a lean engineering team, where you'll work directly with senior technical leadership and have a genuine say in how the data architecture and platform evolve.

The Engineering Challenge

You’ll own and evolve an AWS-native data environment, improving the reliability, scalability and performance of existing data infrastructure while building the foundations for future ML and AI initiatives.

The current stack includes:

• Data Engineering: Apache Iceberg, Kafka, Parquet and Amazon Athena
• Cloud Infrastructure: AWS-native data lake architecture, data ingestion, processing and storage
• Architecture: AWS-based data lake (~5TB), with opportunities to modernise pipelines, optimise performance and improve platform maturity
• ML & AI Integration: Supporting production ML workflows through SageMaker, with future opportunities around GenAI and LLM-powered diagnostic capabilities

What You’ll Own

• Designing, building and maintaining scalable, production-grade data pipelines across batch and streaming workloads
• Evolving the AWS data lake architecture, improving data quality, reliability, performance and accessibility
• Building and optimising data ingestion, transformation and processing workflows using modern data engineering technologies
• Working directly with data scientists, ML engineers and architects to ensure reliable, high-quality data is available for production ML systems
• Influencing architectural decisions, establishing engineering best practices and shaping the next generation of data infrastructure

What We’re Looking For

• Strong hands-on experience building and operatingproduction data engineering systems
• Deep familiarity withAWS and cloud-native data architectures
• Strong Python and SQL skills, with solid software engineering fundamentals
• Experience designing and maintaining scalable ETL/ELT pipelines, data lakes or modern lakehouse architectures
• Understanding of distributed data processing, streaming architectures and data pipeline optimisation
• Someone who enjoys end-to-end ownership, technical problem-solving and working without layers of bureaucracy
• Experience withApache Iceberg, Kafka, Parquet or Athena is highly desirable, while exposure to SageMaker or ML infrastructure is a bonus -production data engineering expertise is the priority .