Lead Data Engineer (Contract, Full-Time) [HR208] (UK)

Smart Working Solutions

Greater LondonRemoteContract

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About Smart Working At Smart Working, we believe your job should not only look right on paper but also feel right every day. This isn’t just another remote opportunity - it’s about finding where you truly belong, no matter where you are. From day one, you’re welcomed into a genuine community that values your growth and well-being. Our mission is simple: to break down geographic barriers and connect skilled professionals with outstanding global teams and products for full-time, long-term roles. We help you discover meaningful work with teams that invest in your success, where you’re empowered to grow personally and professionally. Join one of the highest-rated workplaces on Glassdoor and experience what it means to thrive in a truly remote-first world. About the Role As a Lead Data Engineer, you will build and lead the data infrastructure powering an intelligent AI assistant that automates operational workloads for property managers, letting agents and build-to-rent teams. The platform handles communications, compliance, maintenance coordination and scheduling, helping property businesses operate with the efficiency of modern digital platforms. You will architect and scale the data systems behind the AI products, covering real-time data pipelines, analytics infrastructure, vector databases and machine learning data workflows. Working closely with AI engineers, backend engineers, product teams, founders and product leadership, you will ensure the platform can process large volumes of operational data reliably, efficiently and intelligently. As the first senior data hire, you will play a key role in defining the data architecture, technology stack, engineering standards and ways of working. You will also help shape the data team, establishing its culture and hiring bar as the function grows. Responsibilities Architect and build scalable data pipelines and infrastructure to support AI and product systems. Design and maintain data ingestion, transformation and storage architectures for operational and AI workloads. Develop and manage batch and real-time data pipelines. Build and optimise systems for vector search, retrieval and machine learning data pipelines. Ensure data reliability, security and governance across the platform. Collaborate with AI and backend engineering teams to support training, inference and product features. Implement monitoring, observability and data quality frameworks. Optimise the performance of large-scale datasets and query systems. Contribute to technical architecture decisions and long-term data strategy. Act as the founding data hire, defining culture, standards and the hiring bar for the data function as it scales. Partner directly with founders and product leadership to translate data capabilities into product decisions. Requirements 7+ years of professional experience, with the majority of that experience in dedicated data engineering roles. Strong experience designing and building data pipelines and distributed data systems. Experience working with relational databases, with PostgreSQL preferred, although MySQL or similar is acceptable. Experience working with NoSQL databases. Experience with vector databases used in modern AI systems. Strong programming experience in Python. Demonstrated ability to make and justify architectural decisions, rather than only implementing them. Experience building scalable backend systems. Experience designing data models and storage architectures. Strong understanding of data processing performance and optimisation. Experience with some of the following data frameworks and infrastructure technologies is highly desirable: Apache Spark, Apache Airflow, Kafka, and Elasticsearch or OpenSearch. Experience with relevant database technologies is highly desirable, including PostgreSQL, MongoDB, and vector databases such as Qdrant, Milvus or pgvector. Experience with Python data-processing libraries such as Pandas or Polars is highly desirable. Nice to Have Experience working on AI or machine learning platforms. Familiarity with stream processing and event-driven architectures. Experience with cloud infrastructure such as GCP, AWS or Azure. Experience working in high-growth startups or early-stage companies.

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