> Markdown version of [/jobs/ext/4663916-senior-data-engineer](https://www.wearedevelopers.com/jobs/ext/4663916-senior-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Engineer - **Company:** Understanding Recruitment - **Location:** Greater London, United Kingdom - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Apache HTTP Server, Systems Engineering, Cloud Computing, Data Architecture, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Distributed Computing Environment, Python (Programming Language), Machine Learning, Operational Databases, Raw Data, Cloud Services, Standard Sql, Parquet, Data Ingestion, Large Language Models, Event Driven Architecture, Data Lakes, AWS Data Analytics, Apache Kafka, Machine Learning Operations, Data Pipelines - **Published:** November 11, 2026 - **Apply:** https://www.understandingrecruitment.com/job/senior-data-engineer-9321/ ## About the Role * Strong hands-on experience building and operating production data engineering systems * Deep familiarity with AWS 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 with Apache 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. ## Description Senior Data Engineer We're hiring a Senior 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 operating production data engineering systems * Deep familiarity with AWS 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 with Apache 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.